text:
This article needs additional citations for verification. (September 2014)
Connacht or Connaught (/ ( ) /, KON-awt, KON-ə(kh)t; Irish: Connachta [ˈkʊn̪ˠəxt̪ˠə] or Cúige Chonnacht [ˌkuːɟə ˈxʊn̪ˠəxt̪ˠ]), is one of the four provinces of Ireland, in the west of Ireland. Until the ninth century it consisted of several independent major Gaelic kingdoms (Uí Fiachrach, Uí Briúin, Uí Maine, Conmhaícne, and Delbhna).
|• Teachtaí Dála||6 Independent TDs |
5 Fine Gael TDs
4 Fianna Fáil TDs
4 Sinn Féin TDs
|• MEPs[a]||2 Fine Gael MEPs|
1 Sinn Féin MEP
1 Independent MEP
|• Total||17,711 km2 (6,838 sq mi)|
|• Density||33/km2 (86/sq mi)|
|Time zone||UTC±0 (WET)|
|• Summer (DST)||UTC+1 (IST)|
|Eircode routing keys|
Beginning with F, H, N (primarily)
|Telephone area codes||07x, 09x (primarily)|
|ISO 3166 code||IE-C|
|a. ^ Connacht is part of the Midlands–North-West constituency; the five Connacht counties contain 36.2% of the population of this constituency.|
Between the reigns of Conchobar mac Taidg Mór (died 882) and his descendant, Aedh mac Ruaidri Ó Conchobair (reigned 1228–33), it became a kingdom under the rule of the Uí Briúin Aí dynasty, whose ruling sept adopted the surname Ua Conchobair. At its greatest extent, it incorporated the often independent Kingdom of Breifne, as well as vassalage from the lordships of western Mide and west Leinster. Two of its greatest kings, Tairrdelbach Ua Conchobair (1088–1156) and his son Ruaidri Ua Conchobair (c. 1115–1198) greatly expanded the kingdom's dominance, so much so that both became High King of Ireland.
The Kingdom of Connacht collapsed in the 1230s because of civil war within the royal dynasty, which enabled widespread Hiberno-Norman settlement under Richard Mór de Burgh, 1st Baron of Connaught, and his successors. The Norman colony in Connacht shrank from c. 1300 to c. 1360, with events such as the 1307 battle of Ahascragh (see Donnchad Muimnech Ó Cellaigh), the 1316 Second Battle of Athenry and the murder in June 1333 of William Donn de Burgh, 3rd Earl of Ulster, all leading to Gaelic resurgence and colonial withdrawal to towns such as Ballinrobe, Loughrea, Athenry, and Galway. Well into the 16th century, kingdoms such as Uí Maine and Tír Fhíacrach Múaidhe remained beyond English control, while many Norman families such as de Burgh, de Bermingham, de Exeter, de Staunton, became entirely Gaelicised. Only in the late 1500s, during the Tudor conquest of Ireland, was Connacht shired into its present counties.
Connacht's population was 1,418,859 in 1841. Then came the Great Famine of the 1840s, which began a 120-year decline to under 400,000. The province has a population of just under 590,000 according to the preliminary results of the 2022 census.
British cultural imperialism was weaker in the west of Ireland, and Connacht today has the highest number of Irish language speakers among the four Irish provinces. Currently, the total percentage of people who consider themselves as Irish speakers in Connacht is 39.8% (more than 202,000 persons). There are Gaeltacht areas in Counties Galway and Mayo.
The province of Connacht has no official function for local government purposes, but it is an officially recognised subdivision of the Irish state. It is listed on ISO-3166-2 as one of the four provinces of Ireland and "IE-C" is attributed to Connacht as its country sub-division code. Along with counties from other provinces, Connacht lies in the Midlands–North-West constituency for elections to the European Parliament.
The name comes from the medieval ruling dynasty, the Connacht, later Connachta, whose name means "descendants of Conn", from the mythical king Conn of the Hundred Battles. The name of the province in the Irish language is Connachta. Originally Connacht was a singular collective noun, but it came to be used only in the plural Connachta, partly by analogy with plural names of other dynastic territories like Ulaid and Laigin, and partly because the Connachta split into different branches. Before the Connachta dynasty, the province (cúige, "fifth") was known as Cóiced Ol nEchmacht. In Modern Irish, the province is usually called Cúige Chonnacht, "the Province of Connacht", where Chonnacht is plural genitive case with lenition of the C to Ch.
The usual English spelling in Ireland since the Gaelic revival is Connacht, the spelling of the disused Irish singular. The official English spelling during English and British rule was the anglicisation Connaught, pronounced // or //. This was used for the Connaught Rangers in the British Army; in the title of Queen Victoria's son Arthur, Duke of Connaught; and the Connaught Hotel, London, named after the Duke in 1917. Usage of the Connaught spelling is now in decline. State bodies use Connacht, for example in Central Statistics Office census reports since 1926, and the name of the Connacht–Ulster European Parliament constituency of 1979–2004, although Connaught occurs in some statutes. Among newspapers, the Connaught Telegraph (founded 1830) retains the anglicised spelling in its name, whereas the Connacht Tribune (founded 1909) uses the Gaelic. Connacht Rugby who represent the region and are based in Galway, use the Gaelic spelling also.
Geography and political divisions Edit
The province is divided into five traditional counties, the fewest of any province. These are: Galway, Leitrim, Mayo, Roscommon and Sligo. Connacht is the smallest of the four Irish provinces both in terms of size and population. Galway is the only official city in the province.
|Galway (Gaillimh)||276,451||6,149 km2 (2,374 sq mi)|
|Leitrim (Liatroim)||35,087||1,590 km2 (610 sq mi)|
|Mayo (Maigh Eo)||137,231||5,586 km2 (2,157 sq mi)|
|Roscommon (Ros Comáin)||69,995||2,548 km2 (984 sq mi)|
|Sligo (Sligeach)||69,819||1,838 km2 (710 sq mi)|
|Grand Total||588,583||17,711 km2 (6,838 sq mi)|
Largest settlements Edit
Largest population centres of Connacht
|Rank||City name||County||Pop.||Rank||City name||County||Pop.|
|1||Galway||County Galway||79,934||11||Monksland||County Roscommon||4,978|| |
|2||Sligo||County Sligo||19,199||12||Athenry||County Galway||4,445|
|3||Castlebar||County Mayo||12,068||13||Carrick-on-Shannon||County Leitrim||4,062|
|4||Ballina||County Mayo||10,171||14||Claremorris||County Mayo||3,687|
Physical geography Edit
Much of the west coast (e.g. Connemara and Erris) is ruggedly inhospitable and not conducive for agriculture. It contains the main mountainous areas in Connacht, including the Twelve Bens, Maumturks, Mweelrea, Croagh Patrick, Nephin Beg, Ox Mountains, and Dartry Mountains.
Killary Harbour, one of Ireland's fjords (the others being Carlingford Lough and Lough Swilly), is located at the foot of Mweelrea. Connemara National Park is in County Galway. The Aran Islands, featuring prehistoric forts such as Dún Aonghasa, have been a regular tourist destination since the 19th century.
Inland areas such as east Galway, Roscommon and Sligo have enjoyed greater historical population density due to better agricultural land and infrastructure.
Early history Edit
Up to the early historic era, Connacht then included County Clare, and was known as Cóiced Ol nEchmacht. Later myths state the Fir Bolg ruled all Ireland before the Tuatha Dé Danann arrived. When the Fir Bolg were defeated, the Tuatha Dé Danann drove them to Connacht. Sites such as the Céide Fields, Knocknarea, Listoghil, Carrowkeel Megalithic Cemetery and Rathcroghan, all demonstrate intensive occupation of Connacht far back into prehistory. Enigmatic artefacts such as the Turoe stone and the Castlestrange stone, whatever their purpose, denote the ambition and achievement of those societies, and their contact with the La Tène culture of mainland Europe. In the early historic era (c. A.D. 300 – c. A.D. 600), Ol nEchmacht was not a united kingdom but a region. It comprised dozens of major and minor túath; rulers of larger túatha (Maigh Seóla, Uí Maine, Aidhne and Máenmaige) were accorded high-king status, while peoples such as the Gailenga, Corco Moga and Senchineoil were lesser peoples given the status of Déisi. All were termed kingdoms, but according to a graded status, denoting each according the likes of lord, count, earl, king.
Some of the more notable peoples or ethnic groups included the following:
- Conmaicne – west coast and northern areas of County Galway
- Dartraige – north-west County Leitrim
- Delbhna – south County Roscommon, and both sides of the Lough Corrib
- Fir Craibe – County Clare (then part of Connacht) and south-west Galway
- Fir Domnann – west coast of Mayo
- Soghain – most of east-central County Galway
By the 5th century, the pre-historic nations such as the Auteini and Nagnatae – recorded by Ptolemy (c. AD 90 – c. 168) in Geography – gave way to dynasties. This is demonstrated in the noun moccu in names such as Muirchu moccu Machtheni, which indicated a person was of the Machtheni people. As evidenced by kings such as Mac Cairthinn mac Coelboth (died 446) and Ailill Molt (died c. 482), even by the 5th century the gens was giving way to kinship all over Ireland, as both men were identified as of the Uí Enechglaiss and Uí Fiachrach dynasties, not of tribes. By 700, moccu had been entirely replaced by mac and hua (later Mac and Ó).
The name Connacht arose from the most successful of these early dynasties, The Connachta. By 1050, they had extended their rule from Rathcroghan in north County Roscommon to large areas of what are now County Galway, County Mayo, County Sligo, and County Leitrim. The dynastic term was from then on applied to the overall geographic area containing those counties, and has remained so ever since.
Kingdom of Connacht Edit
The most successful sept of the Connachta were the Ó Conchobair of Síol Muireadaigh. They derived their surname from Conchobar mac Taidg Mór (c. 800 – 882), from whom all subsequent Ó Conchobair Kings of Connacht descended.
Conchobar was a nominal vassal of Máel Sechnaill mac Máele Ruanaid, High King of Ireland (died 862). He married Máel Sechnaill's daughter, Ailbe, and had sons Áed mac Conchobair (died 888), Tadg mac Conchobair (died 900) and Cathal mac Conchobair (died 925), all of whom subsequently reigned. Conchobar and his sons' descendants expanded the power of the Síol Muiredhaigh south into Uí Maine, west into Iar Connacht, and north into Uí Fiachrach Muaidhe and Bréifne.
By the reign of Áed in Gai Bernaig (1046–1067), Connacht's kings ruled much what is now the province. Yet the Ó Conchobair's contended for control with their cousins, the Ua Ruairc of Uí Briúin Bréifne. Four Ua Ruairc's achieved rule of the kingdom – Fergal Ua Ruairc (956–967), Art Uallach Ua Ruairc (1030–1046), Áed Ua Ruairc (1067–1087), and Domnall Ua Ruairc (1098–1102). In addition, the usurper Flaithbertaigh Ua Flaithbertaigh gained the kingship in 1092 by the expedient of blinding King Ruaidrí na Saide Buide. After 1102 the Ua Ruairc's and Ua Flaithbertaigh's were suborned and confined to their own kingdoms of Bréifne and Iar Connacht. From then until the death of the last king in 1474, the kingship was held exclusively by the Ó Conchobair's.
The single most substantial sub-kingdom in Connacht was Uí Maine, which at its maximum extent enclosed central and south County Roscommon, central, east-central and south County Galway, along with the territory of Lusmagh in Munster. Their rulers bore the family name Ó Ceallaigh; its spelling sometimes varying slightly from scribe to scribe.
Though the Ó Ceallaigh's were never elevated to the provincial kingship, Uí Maine existed as a semi-independent kingdom both before and after the demise of the Connacht kingship.
Kings and High Kings Edit
Under kings Tairrdelbach Ua Conchobair (1088–1156) and his son Ruaidrí Ua Conchobair (c. 1120–1198) Connacht became one of the five dominant kingdoms on the island. Tairrdelbach and Ruaidrí became the first men from west of the Shannon to gain the title Ard-Rí na hÉireann (High King of Ireland). In the latter's case, he was recognised all over the island in 1166 as Rí Éireann, or King of Ireland.
Tairrdelbach was highly innovative, building the first stone castles in Ireland, and more controversially, introducing the policy of primogeniture to a hostile Gaelic polity. Castles were built in the 1120s at Galway (where he based his fleet), Dunmore, Sligo and Ballinasloe, where he dug a new six-mile canal to divert the river Suck around the castle of Dun Ló. Churches, monasteries and dioceses were re-founded or created, works such as the Corpus Missal, the High Cross of Tuam and the Cross of Cong were sponsored by him.
Tairrdelbach annexed the Kingdom of Mide; its rulers, the Clann Cholmáin, became his vassals. This brought two of Ireland's five main kingdoms under the direct control of Connacht. He also asserted control over Dublin, which was even then being recognised as a kind of national capital.
His son, Ruaidrí, became king of Connacht "without any opposition" in 1156. One of his first acts as king was arresting three of his twenty-two brothers, "Brian Breifneach, Brian Luighneach, and Muircheartach Muimhneach" to prevent them from usurping him. He blinded Brian Breifneach as an extra precaution.
Ruaidrí was compelled to recognise Muirchertach Mac Lochlainn as Ard-Rí, though he went to war with him in 1159. Mac Lochlainn's murder in 1166 left Ruaidrí the unopposed ruler of all Ireland. He was crowned in 1166 at Dublin, "took the kingship of Ireland ...[and was] inaugurated king as honourably as any king of the Gaeidhil was ever inaugurated;" He was the first and last native ruler who was recognised by the Gaelic-Irish as full King of Ireland.
However, his expulsion of Dermot MacMurrough later that year brought about the Norman invasion of Ireland in 1169. Ruaidrí's inept response to events led to rebellion by his sons in 1177, and his deposition by Conchobar Maenmaige Ua Conchobair in 1183. Ruaidrí died at Cong in 1198, noted as the annals as late "King of Connacht and of All Ireland, both the Irish and the English."
High medieval era Edit
Connacht was first raided by the Anglo-Normans in 1177 but not until 1237 did encastellation begin under Richard Mor de Burgh (c. 1194–1242). New towns were founded (Athenry, Headford, Castlebar) or former settlements expanded (Sligo, Roscommon, Loughrea, Ballymote). Both Gael and Gall acknowledged the supreme lordship of the Earl of Ulster; after the murder of the last earl in 1333, the Anglo-Irish split into different factions, the most powerful emerging as Bourke of Mac William Eighter in north Connacht, and Burke of Clanricarde in the south. They were regularly in and out of alliance with equally powerful Gaelic lords and kings such as Ó Conchobair of Síol Muireadaigh, Ó Cellaigh of Uí Maine and Mac Diarmata of Moylurg, in addition to extraprovincial powers such as Ó Briain of Thomond, FitzGerald of Kildare, Ó Domhnaill of Tír Chonaill.
Lesser lords of both ethnicities included Mac Donnchadha, Mac Goisdelbh, Mac Bhaldrin, Mac Siurtain, Ó hEaghra, Ó Flaithbeheraigh, Ó Dubhda, Ó Seachnasaigh, Ó Manacháin, Seoighe, Ó Máille, Ó Ruairc, Ó Madadháin, Bairéad, Ó Máel Ruanaid, Ó hEidhin, Ó Finnaghtaigh, Ó Fallmhain, Breathneach, Mac Airechtaig, Ó Neachtain, Ó hAllmhuráin, Ó Fathaigh.
The town of Galway was the only significant urban area in the province. Its inhabitants governed themselves under charter of the king of England. Its merchant families, The Tribes of Galway, traded not only with the lordships around them and in Ireland, but with England, France, and Spain. Its mayor enjoyed supreme power but only for the length of his office, rarely more than a year. Galway's inhabitants were of mixed descent, its families bearing surnames of Gaelic, French, English, Welsh, Norman and other origins.
Connacht was the site of two of the bloodiest battles in Irish history, the Second Battle of Athenry (1316) and the Battle of Knockdoe (1504). The casualties of both battles were measured in several thousand, unusually high for Irish warfare. A third battle at Aughrim in 1691 resulted in an estimated 10,000 deaths.
All of Connacht's lordships remained in states of full or semi-independence from other Gaelic-Irish and Anglo-Irish rulers until the late 16th century, when the Tudor conquest of Ireland (1534–1603) brought all under the direct rule of King James I of England. The counties were created from c. 1569 onwards.
Confederate and Williamite Wars Edit
During the 17th century representatives from Connacht played leading roles in Confederate Ireland and during the Williamite War in Ireland. Its main town, Galway, endured several sieges (see Sieges of Galway), while warfare, plague, famine and sectarian massacres killed about a third of the population by 1655. One of the last battles fought in pre-20th century Ireland occurred in Connacht, the Battle of Aughrim on 12 July 1691.
Early modern era Edit
Connacht was mainly at peace between 1691 and 1798. In 1798 Connacht was a major backdrop to the Irish Rebellion of 1798 when French forces under General Jean Humbert of the French Republic landed in Killala, County Mayo to link up with the United Irishmen. Together, the French and Irish forces defeat a British garrison at the Races of Castlebar before proclaiming the Irish Republic, which later became better known as the "Republic of Connacht" as its area of effective control never extended beyond the province. The Republic, and the Rebellion itself, was effectively crushed at the Battle of Ballinamuck.
A population explosion in the early 18th century was curbed by the Irish Famine, which led to many deaths and some emigration. Its memory has been overshadowed by the Great Famine (Ireland) one hundred years later.
The Famine to World War One Edit
Connacht was the worst hit area in Ireland during the Great Famine, in particular, counties Mayo and Roscommon. In the Census of 1841, the population of Connacht stood at 1,418,859, the highest ever recorded. By 1851, the population had fallen to 1,010,031 and would continue to decline until the late 20th century.
Irish language Edit
The Irish language is spoken in the Gaeltacht areas of Counties Mayo and Galway, the largest area being in the west of County Galway. The Galway Gaeltacht is the largest Irish-speaking region in Ireland, taking in Cois Fharraige, parts of Connemara, Conamara Theas, the Aran Islands, Dúithche Sheoigeach (Joyce Country) and the Galway City Gaeltacht. Irish-speaking areas in County Mayo can be found in Iorras, Acaill and Tourmakeady.
According to the 2016 census, Irish is spoken outside of the education system on a daily basis by 9,455 people in the Galway County Gaeltacht areas.
There are 202,667 Irish speakers in the province, over 84,000 in Galway and more than 55,000 in Mayo. There is also the 4,265 attending the 18 Gaelscoileanna (Irish language primary schools) and three Gaelcholáiste (Irish language secondary schools) outside the Gaeltacht across the province. Between 7% and 10% of the province are either native Irish speakers from the Gaeltacht, in Irish medium education or native Irish speakers who no longer live in Gaeltacht areas but still live in the province.
Gaelic games Edit
Gaelic football is played throughout the province with the five counties annually competing in the Connacht Senior Football Championship to determine the provincial champion. Galway are the most successful side in Connacht with 48 Connacht titles and 9 All-Ireland Senior Football Championship. Mayo have been the dominant force in the province in recent years winning a five-in-a-row of Connacht titles from 2011 to 2015, and have regularly reached the semi-finals and finals of the All-Ireland Senior Football Championship. No football team from Connacht has won the All-Ireland since Galway in 2001.
Hurling in Connacht mostly played in County Galway. Galway is the only team in the province to compete in the All-Ireland Senior Hurling Championship winning the Liam MacCarthy Cup five times. The Galway hurling team compete in the Leinster Senior Hurling Championship due to the lack of competition in the province.
Rugby union Edit
Connacht is represented by Connacht Rugby in the Pro14 and the Rugby Champions Cup. Connacht home games are played in the Galway Sportsgrounds in Galway. During the 2015/2016 Season of the Pro12, Connacht, for the first time, reached the play off stages of the competition and won the final in Edinburgh against rivals Leinster. It was their first ever Pro12 title.
Other sports Edit
Some other sports are overseen by provincial bodies, including in association football, where the Connacht Football Association is the governing body for a number of Connacht league and cup competitions. Traditionally there have been two main senior men's teams from the province that compete on a national level, Galway United F.C. and Sligo Rovers F.C. Both clubs have won various domestic honours.
Cricket is a minor, but growing, sport within the province. The Connacht Cricket Union, founded in 2010, is the governing body for cricket in the province. There are cricket clubs based in Ballaghaderreen, Ballyhaunis, Galway, and Sligo. Connacht does not currently enter a team into the provincial competitions.
See also Edit
- Galway city
- Connacht Senior Football Championship
- Grace O'Malley
- Kings of Umaill
- Kings of Uí Fiachrach Muaidhe
- Kings of Uí Maine
- Kings of Luighne Connacht
- Kings of Sliabh Lugha
- Corca Fhir Trí
- List of Cities and Towns in Connacht by population
- Coin of Connaught
- The Connaught Rangers
- Duke of Connaught
- Kings of Connacht
- Lords of Connaught
- "Connacht (also Connaught)". foclóir.ie. Foras na Gaeilge. Retrieved 16 December 2022.
- "Population by province". Central Statistics Office. 2016. Archived from the original on 31 October 2018. Retrieved 30 October 2018.
- "ISO 3166-2 Newsletter II-1 (including Irish language names for provinces and counties)" (PDF). International Organization for Standardization (ISO). 19 February 2010. p. 20. Archived (PDF) from the original on 3 February 2017. Retrieved 30 December 2012.
- Census of Ireland 2016: 550,742 out of 1,521,592 total.
- "Connacht" (US) and "Connacht". Lexico UK English Dictionary. Oxford University Press. Archived from the original on 11 February 2020.
- "Connacht". Merriam-Webster Dictionary. Retrieved 11 January 2019.
- "Connacht". The American Heritage Dictionary of the English Language (5th ed.). HarperCollins. Retrieved 11 October 2019.
- The National Cyclopaedia of Useful Knowledge, Vol IV. London: Charles Knight. 1848. p. 858.
- "Geographic Changes". Central Statistics Office. Retrieved 3 January 2023.
- "Percentage of Irish Speakers and Non-Irish Speakers Aged 3 Years and Over 2011 to 2016 by Sex, County and City, Statistical Indicator and Census Year". Central Statistics Office. Archived from the original on 10 July 2017. Retrieved 31 October 2018.
- O'Rahilly, T. F. (1942). "Notes, Mainly Etymological". Ériu. Royal Irish Academy. 13: 157. JSTOR 30006811.
- Wells, John C. (2008). Longman Pronunciation Dictionary. Pearson Longman. s.v. Connacht, Connaught. ISBN 978-1-4058-8117-3.
- "Population of Saorstát Éireann and of each Province at each Census since 1881 and the Numbers of Marriages, Births and Deaths Registered in each Intercensal Period since 1871" (PDF). Census 1926 Volume 1 – Population, Area and Valuation of each DED and each larger Unit of Area. CSO. Archived (PDF) from the original on 14 November 2012. Retrieved 15 June 2014.
- "European Assembly Elections Act, 1977, Schedule 2". Irish Statute Book. Archived from the original on 27 July 2014. Retrieved 15 June 2014.
- "European Parliament Elections Act, 1993, Section 9". Irish Statute Book. Archived from the original on 27 July 2014. Retrieved 15 June 2014.
- "European Parliament Elections Act, 1997, Schedule 3". Irish Statute Book. Archived from the original on 27 July 2014. Retrieved 15 June 2014.
- "S.I. No. 91/2014 – Statistics (Carriage of Passengers, Freight and Mail by Air) Order 2013". Irish Statute Book. Archived from the original on 27 July 2014. Retrieved 15 June 2014.
- "S.I. No. 200/1987 – Garda Síochána (Associations) (Superintendents and Chief Superintendents) Regulations, 1987". Irish Statute Book. Archived from the original on 27 July 2014. Retrieved 15 June 2014.
- "Connacht Rugby Website". www.connachtrugby.ie. Archived from the original on 25 July 2016. Retrieved 28 July 2016.
- "Table B – Population of administrative counties, 2011 and 2016". Central Statistics Office. Archived from the original on 24 September 2021. Retrieved 31 October 2018.
- "Population and Birthplace 2016 by Alphabetical List of Towns, CensusYear and Statistic". Central Statistics Office. Archived from the original on 24 September 2021. Retrieved 4 November 2018.
- "Part 2 of MAC CARTHAIGH'S BOOK". Archived from the original on 14 September 2001.
- "Ó Ceallaigh – Irish Names and Surnames". Archived from the original on 24 September 2021. Retrieved 1 April 2016.
- "1861 Census: Decline of the Population in Ireland (Famine, Disease and Emigration)". www.libraryireland.com. Archived from the original on 28 January 2020. Retrieved 9 February 2020.
- "Government parties hope to woo electorate during six months". The Irish Times. 5 January 2004. Archived from the original on 24 September 2021. Retrieved 6 November 2018.
- "Census of Population 2016 – Profile 10 Education, Skills and the Irish Language. Irish Language and the Gaeltacht. The Gaeltacht". Central Statistics Office. Archived from the original on 8 December 2020. Retrieved 6 November 2018.
- "Percentage of Irish Speakers and Non-Irish Speakers Aged 3 Years and Over 2011 to 2016 by Sex, County and City, Statistical Indicator and Census Year". Central Statistics Office. Archived from the original on 10 July 2017. Retrieved 6 November 2018.
- "Infographic: The number of GAA clubs in every county in Ireland and every continent around the world". Archived from the original on 25 November 2015. Retrieved 24 November 2015.
- Rooney, Declan (19 July 2015). "Mayo crush Sligo for Connacht five-in-a-row". RTÉ.
- "Dublin have had better preparation ahead of Leinster final clash with Galway claims Cathal Moore". 6 July 2013. Archived from the original on 25 November 2015. Retrieved 24 November 2015.
- "Cricket Ireland expands into the west". The Irish Times. 8 November 2010. Archived from the original on 12 April 2019. Retrieved 26 October 2018.

domain: history


Place One Call and Access Our Extensive Inventory of 10,000 Centerless Grinder Parts and Components. Whether you’re looking for Cincinnati Milacron centerless grinding machine parts, Landis, Bryant, Koyo, Micron, Gold Crown or Van Norman, GCH Tool offers the world’s largest inventory of new parts, stocking over 10,000 new readily available parts and component assemblies for your centerless ...Chat Online
Red Iron Oxide is a profoundly focused dry powder of the finest grade and is in a perfect world appropriate for use in paints, rubber, paper, polishing rouge, ceramic, linoleum, wall paper, plastic asphalt, cConcrete colours, Organic and Inorganic Pigments, Kaolin , Manganese Dioxide, Micaceous Iron oxide, Mica powder, mosaic tiles and flooring. Our each product is manufactured with the use of ...Read More
They are three types of clay used in cement manufacturing, namely silty clay, Zafarana ... The limestone is crushed in the first crusher called a jaw crusher and then fed ... slag (a steel industry by-product) or fly ash (from coal-fired power plants). ... Cement crusher is the important cement manufacturing machine used in cement production ...Read More
2. The frame and spindle of the cone crusher are made of high strength alloy ,finely processed,Durable,with light weight and more output. For the same output, the volume of the cone crusher is smaller than ordinary one. 3. Single cylinder hydraulic adjustment and protection is safe and reliable. Hydraulic pressure and lubrication are combined ...Read More
Sugarcane leaves, Soybean stalk, wood thin waste, veneer waste are available in big size, so it is not feed able for Briquetting machine. It's require to cut below 10 mm to 15mm. Hammer mill is the best solution to sizing any kind of Biomass as per required size.Read More
Khumani Iron Ore Mine Jobs in South Africa Jobs 1-10 of 2364. Locations Categories Engineering (375) Other / general (326) IT / Computing / Software (153) Scientific / QA (128) Graduate (72) Management / exec / consulting (59) Accounting / Finance (56) Employers ...Read More
VSI crusher is a machine that produces high efficiency sands, which is developed after several technical improvements, based on the introduction of advanced German technology. It is the Preferred option in the field of the manufacture of artificial sands and the modeling of aggregates.Read More
Operating mill ball charge 15 percent by volume ... On a large dual pinion mill, using the ABB drive to rotate the mill for maintenance is an advantage over an inching drive, because the inching torque is transmitted by the dual drive system to the ... Figure 9: Smooth starting of dual pinion mill drive system (Copper Mountain Ball Mill 1)Read More
The Group also holds a stake in Damnaz Cement Company Ltd which is the Majority Shareholder in Cement Company of Northern Nigeria. The Group’s plants have the capacity to Provides various grades of cement as required in the local Nigeria markets and meet the highest standard of cement …Read More
Oct 04, 2013 The following is a stone by stone analysis of prevention and treatment techniques of the different types of kidney stones. Calcium oxalate stones This is the most common kidney stone that people present to the Hoffman Center with. Much can be done to prevent them. The major inhibitor of calcium stones is citrate.Read More
HOOSIER GOLD DADDY Mini Wash Plant concentrator W/Shaker DIY ... Also the classifier section will work wet or dry. HOOSIER GOLD... Read more. Dry Washers For Gold Mining,Best Dold Dry Washer. Dry clean gold back to the earliest days of the discovery of gold, where the water was ... Using three different processes simultaneously, dry washer ...Read More
Shanghai Esong Mining Machinery Co., Ltd. ... Impact Crusher  Chemical Mixing Machine  Sand Making Machine  Wet Magnetic Separator  Sand Vibrating Screen  Flotation Machine  Vibrating Screen [1 ...Read More
Sourcing Guide for Stone Crushing Plants: China manufacturing industries are full of strong and consistent exporters. We are here to bring together China factories that supply manufacturing systems and machinery that are used by processing industries including but not limited to: stone crusher, rock crusher, crusher.Read More
Operation Notes of Roll Crusher for Sale (1) Strengthen the deironing work. The non-crushable materials falling into the space between the two rolls will damage the crusher, which will stop the machine, for this reason, it is necessary to install a deironing device in front of the crusher. (2) Sticky materials can easily block the crushing space.Read More
Offering wide range of crusher like Jaw Crusher, Cone Crusher, Mobile Crusher and many more.. Roljack Asia Ltd. takes pleasure to introduce itself as a prominent manufacturer, exporter and supplier of all types of Construction Equipment, Mining Equipment, Marble Processing Equipment & other allied Heavy Machinery.Read More
We will list a few right here, right now just to show you how incredibly awesome we are at our company!
Centerless Grinder Parts Components: Cincinnati Landis ...
Place One Call and Access Our Extensive Inventory of 10,000 Centerless Grinder Parts and Components. Whether you’re looking for Cincinnati Milacron centerless grinding machine parts, Landis, Bryant, Koyo, Micron, Gold Crown or Van Norman, GCH Tool offers the world’s largest inventory of new parts, stocking over 10,000 new readily available parts and component assemblies for your centerless ...Read More
Centerless Grinding Cincinnati 3D Cad Model Library ...
old transmission of centerless grinding cincinnati . The Computer-Aided Design ( CAD ) files and all associated content posted to this website are created, uploaded, managed …Read More
Centerless Grinders For Sale New &Amp; Used
CINCINNATI MODEL 220-8LR CENTERLESS GRINDER REMANUFACTURED BY CINCINNATI IN 1990 S/N 2M2H5KE-32 NEW 1961 CAPACITY ----- 3 DIAMETER CAPACIT. Maximum workpiece diameter: 4.75 Grinding wheel diameter: 20Read More
Centerless Grinder Centerless Grinding Machine Used Cnc ...
Cincinnati Centerless Grinding Machine,Used Cincinnati 340-20 Centreless Grinder for Sale,Delhi,India. A grinding machine is basically used for removing the excess or unwanted material from a workpiece. There are many types of grinding machines available and one of these is cnc centerless grinding machine. The all models of cnc centerless ...Read More
Centerless Grinding Wheels Norton Abrasives
Achieve extremely close tolerances with Norton centerless grinding wheels. Whether through feed, end feed or in feed grinding, our centerless grinding wheels give a precision finish.Read More
Centerless Grinding Staightening Polishing Close ...
M & M Quality Grinding provides a complete range of centerless grinding, polishing and straightening services. We will grind stainless steel, aluminum, brass, carbon steel, carbon steel, alloys, and other metals. Centerless Grinding (13) Cincinnati #2 centerless grinding/polish machines 1/16 to 7.000 Diameter Lengths to 36 feetRead More
Centerless Grinding -
Centerless Grinding Like cylindrical grinding, centerless grinding also involves rotating a workpiece against a high-speed grinding wheel. However, in this method, the object is not secured with chucks or centers, making it more appropriate for solid parts that lack a hollow center.Read More
Used Twin Grip Cnc Centerless Grinding For Sale ...
3-500 CINCINNATI TWIN GRIP CENTERLESS GRINDER STANDARD SPECIFICATIONS Grinding wheel size: 24” x 20” x12” Regulating wheel size: 14” x 20” x 6” Work O.D. range: 1/4 to 6 Grinding wheel drive: 40 HP AC Motor Gri...Read More
Midwest Centerless Grinding Co. Cincinnati Ohio Oh 45214
Midwest Centerless Grinding provides high quality in centerless grinding by working within very tight tolerances and using a variety of materials including tool steel, carbon fiber material, some types of ceramic materials, plastic, chrome and aluminum. For assistance with your next centerless grinding …Read More
Cincinnati Centerless Grinders For Sale New &Amp; Used ...
325-12 CINCINNATI MILACRON 325-12 CINCINNATI CENTERLESS GRINDER Model DE, New 1972, S/N 350412H5Z-0024 Stock# K10997 EQUIPPED WITH: Automatic Hydraulic Grinding Wheel Truing Unit, Regulating Wheel P Cincinnati, OH View ListingRead More
Cincinati Tyrolit Centerless Grinding Wheels
Cincinnati Tyrolit Centerless Grinding Wheels. The centerless grinding operation enables the round grinding of cylindrical workpieces in lengths of just a few millimeters up to several meters with maximum precision and profitability! TYROLIT supplies the complete system of grinding wheel, regulating wheel and dressing tool. ...Read More
Centerless Grinder Parts Oem Centerless Grinder Parts
Our company has trusted Centerless Grinding Solutions for over 10 years to repair/service our Cincinnati Centerless Grinders. We recently updated (2) of our centerless grinders with servo controls. The increased production along with holding a tighter tolerance will be a great return on investment.Read More
Centerless Grinding In Southern Ohio (Oh) On …
Feb 20, 2021 Precision centerless grinding services for metal, plastic & ceramic materials. Capabilities include centerless bar grinding from 1/8 in. x 144 in. to 2 in. x 144 in. diameter , tolerance to 0.0005 in., up to 14 ft. length & finishes to 8-10 Ra, centerless in-feed grinding for complex shapes from 0.050 in. to 3 in. diameter , up to 8 in. length, roundness to 0.0001 in. & finishes to 8-10 micro ...Read More
Centerless Grinding - Profile Grinding Cleveland Ohio
Given the limited scope of set up and speed of material removal, centerless grinding is an efficient alternative to precision turning processes for external cylindrical features, and achieves precision size, surface finish, roundness, and concentricity requirements.Read More
Cincinnati Centerless Grinder Manual Pdf
Jun 27, 2020 In a centerless grinding machine having hrinder grinding wheel and a regulating wheel supported in opposed relation to form a grinding throat therebetween, the combination of a work carrier movable laterally to the axis of the grinding throat for work inserting and removing purposes, said work carrier having a work rest blade mounted thereon ...Read More
Centerless Grinding Service - Throughfeed Profile ...
Centerless Grinding Machines #2 OM Cincinnati Centerless Grinder 1 - Okuma CNC Cylindrical Grinder 15 - Centerless Grinders, including 7 CNCs 220-8 Cincinnati Centerless Grinder 230-10 Cincinnati Centerless Grinder 325-12 Cincinnati Centerless Grinder 340-20 Cincinnati Centerless Grinder Capacity: 12 x 25Read More
Centerless Grinder For Sale In Stock Ebay
Cincinnati No. 2 Centerless Grinding Machine Model OM 220/440V 3 Phase. $4,490.00. or Best Offer. CARBIDE TIPPED CENTERLESS GRINDER BLADE. (G-1-5-3-4) $210.00. Free shipping. or Best Offer. CENTERLESS GRINDER BLADE WORK REST 11 3/4 LONG X …Read More
Centerless Grinding -
Our centerless grinding Department consists of a Cincinnati No. 2 Centerless Grinder set up for Thru-Feed Cylindrical Grinding. We can handle material from .250 up to 4 diameter up to 12 ft. long. Ideal Machining & Supply handles many different types on materials and is …Read More
Precision Cylindrical Grinding - Midwest Centerless
For assistance with your next centerless grinding project, please give us a call at 513-541-8076 or use our online Request A Quote form. Diameters to 10.5 Lengths to 72 Tolerances to .0003 Midwest Centerless Grinding Co. • 1115-17 Straight Street • Cincinnati, OH 45214 • www.midwestcenterless.com Phone: 513.541.8076 • Fax: ...Read More
Centerless Grinder Rebuilders: Upgrades Remanufacturing ...
Centerless Grinder Rebuilding, Upgrades Cincinnati Milacron grinders • Gold Crown grinders • Van Norman grinders and more. When it is time to upgrade a centerless grinder, GCH Machinery is the name that comes to the minds of the world’s most well-respected manufacturers.Read More
Lee\'S Grinding Inc. - Centerless Grinding
Centerless Grinding CAPABILITIES With both in-feed and thru-feed CNC and Conventional Centerless Grinders, we can deliver precision, high-volume parts as well as prototypes. Programmable, rotary diamond dressing facilitates precise grinding of complicated parts, reduces tooling requirements and, ultimately, reduces cost.Read More
Cincinnati Centerless Grinder Manual Pdf
One of the objects of this invention is to provide an improved simplified bench type centerless grinding machine. Cincinnati Centerless Grinder Manuals. A stop screw 89 threaded in a fixed part of the machine serves as a stop to limit the counterclockwise movement of the carrier and determine its grinding position. In a centerless grinding ...Read More


text:
While Liverpool can rest in the knowledge they are once more top at Christmas, Jurgen Klopp will be fixing his gaze on the future.
With no game until December 27, when the Premier League champions host West Brom, the Reds can afford to have themselves a very Merry Christmas.
Unlike last year's 8pm excursion to Leicester, Klopp and his players can simply sit back and watch the Boxing Day results roll in knowing they will still be sitting pretty on top of the tree of English football.
But with back-to-back titles now firmly the objective for Liverpool, Klopp will be looking beyond the end of an unforgettable 2020 to make sure they are best placed to achieve that.
Here are some of the things the club might be looking to consolidate in the New Year.
Get Thiago settled
Thiago Alcantara's Liverpool career is the sum of just 135 minutes to date.
News that the Spain international is back in full training will be a hugely welcome boost, but this is just the start.
In a way, it feels like Liverpool are still waiting on the arrival of the midfielder with only Alex Oxlade-Chamberlain playing less this season.
Getting Thiago settled into this midfield will be huge to ensuring the upper hand is kept in the engine room for the remainder of the campaign.
The former Bayern Munich man is a class act and could be key to keeping Liverpool's name on the Premier League trophy.
Champions League revenge
Liverpool may feel they have some unfinished business to attend to in the Champions League.
The Reds were dumped out in extra time by Atletico Madrid back in March in a game that should not have gone ahead given what is known now around mass gatherings and the spread of COVID-19.
The then holders were on top for much of the game against Los Rojiblancos on March 12, boasting 34 shots and 71 per cent possession.
Liverpool found goalkeeper Jan Oblak to be in inspired form however, while their own shot-stopper, Adrian, made a crucial error to gift the Spaniards the result.
European action resumes in February, with a last-16 tie with RB Leipzig on the horizon and the six-time winners will be determined to muster a more convincing assault on the biggest prize in club football.
The return of fans
The return of football supporters across the country has been a welcome sight for a nation still dealing with a worldwide pandemic.
But while Liverpool and Everton will be able to keep 2,000 supporters inside the stadium for upcoming matches, they are now the only two teams in the Premier League to be able to do so.
The smattering of attendees across the terraces makes a huge difference to the live atmosphere and Reds fans have been in full voice for the games with Wolves and Tottenham this month.
The hope is that over the coming months, that figure of 2,000 will start to rise and get closer to something resembling pre-COVID normality.
With Gini Wijnaldum free to talk to clubs outside of England from January 1, Liverpool have a pressing concern on their hands.
No new contract appears on the horizon for the Dutchman but given his 21 appearances already this season, he remains no less important to Klopp.
If Wijnaldum is not going to stay at the club beyond next summer then some successors will need to be assessed in the recruitment department.
By next summer, the likes of Virgil van Dijk, Fabinho and Naby Keita will have two years or less left on their contract while Mohamed Salah recently hinted that his future is in the hands of Anfield chiefs.
Liverpool have done a sterling job of tying their key names to long-term deals over the past few years but there's perhaps more work to be done.
Sign up to our free LFC newsletter
Get all the latest Liverpool breaking news, team news, transfer rumours, injury updates plus analysis of what's next for the Reds.
You'll also get the latest transfer talk and analysis straight to your inbox every day with our FREE email newsletter.
Anfield Road progress
December should have been the month construction work on the expansion of Anfield Road started.
Those plans were shelved over the summer, placed back by 12 months while the club assessed the short-term future of their finances.
The £60million project was postponed in April, with managing director Andy Hughes saying : “We will use this period to review and consider options."
Liverpool supporters will be hopeful that there is progress to be had on the front over the coming months as club owners Fenway Sports Group take their next big step.
FSG have significantly upgraded the infrastructure inside the club in recent years, building a new £50million training complex in Kirkby four years after opening a new Main Stand at Anfield.
A further 7,000 seats at Anfield Road is now the next big project on that checklist.
Planning application will be submitted in the spring before work begins in December next year, should everything line up as Liverpool would hope.

domain: sports


text:
In “Telling the Story,” Amy Davidson asks why the Washington Post’s Jason Rezaian has been in a Tehran prison for nearly a year, without being publicly charged.
Puzzles and Games Dept.
Test Your Knowledge of the 2010s in Culture
How much of the decade can you actually remember?
How William Gibson Keeps His Science Fiction Real
Midway through his career, the inventor of “cyberspace” turned his attention to a strange new world: the present.
In Congress and at the Justice Department, a Bad Day for Facts, Fairness, and the Future
The procedures and norms that govern two of the most serious processes carried out by government—impeaching a President and reviewing a federal investigation—were shredded.
A Reporter at Large

domain: news


text:
For clinical study results to be considered significant they need to involve large numbers of participants, but with a rare illness like malignant mesothelioma, research by necessity often involves smaller pools of patients. A recent National Cancer Institute study included just ten patients but is still receiving positive attention: It revealed that those receiving a combination of an immune system-boosting drug and targeted immunotherapy realized longer survival times.
LMB-100 Could Prolong Mesothelioma Patients’ Lives
The mesothelioma research was documented in the July 1st, 2020 issue of the journal Science Translational Medicine. It was led by Raffit Hassan, M.D., Chief of the Thoracic and GI Malignancies Branch, and focused on a mesothelin-targeting drug called LMB-100 that was developed by Ira Pastan, M.D., Co-Chief of the Laboratory of Molecular Biology.
Mesothelin-targeting drugs are viewed as having significant potential for the treatment of malignant pleural mesothelioma because they bind to specific cells on the surface of the mesothelium in a way that allows malignancies to be targeted. The recent study was a Phase I trial meant to determine whether LMB-100 was safe, but also included administration of an immune checkpoint inhibitor to facilitate the immune system process.
Test Yielded Positive Response in 4 of 10 Mesothelioma Patients
The combination of one of the immune checkpoint inhibitors, pembrolizumab, with LMB-100 provided a complete response in one of the mesothelioma patients and a partial response in three others. The response was far more effective than what would have been expected from the immune checkpoint inhibitor by itself.
Following these results, staff scientist Qun Jiang, Ph.D. of Dr. Hassan’s lab replicated what they’d found by retesting the drug combination on laboratory animals grafted with human mesothelioma. The mice given both LMB-100 and pembrolizumab experienced greater tumor shrinkage than mice treated with just one of the two protocols.
Though this mesothelioma study will require further exploration, it holds great promise. For information on other treatment options, contact the Patient Advocates at Mesothelioma.net at 1-800-692-8608 today.FREE Mesothelioma Packet

domain: medical


text:
Receive an email alert when additional Pomeranian puppies are added. It's easy and free!
BREED: Pomeranian Puppy
Puppies come with FREE Pet Insurance when adopted from NextDayPets.com! *Please ensure your breeder participates
Member Since: October 2006
• Female • $600
• Female • $475

domain: pet


J&K records 127 fresh COVID-19 cases, 2 related deaths
India records lowest active COVID-19 caseload in 203 days
Srinagar: Jammu and Kashmir on Wednesday recorded 127 fresh cases of coronavirus, taking the cumulative infection count to 3,30,118, while the death toll rose to 4,426 with two fatalities reported from the Valley in the past 24 hours, officials said.
Seventy-three of the fresh cases were from Kashmir and 54 from Jammu division, the officials said.
They said Srinagar district recorded a maximum of 44 new cases followed by 38 cases in Poonch district.
Moreover, 134 more COVID-19 patients have recovered and were discharged from various hospitals including 112 from Kashmir and 22 from Jammu division in the last 24 hours, officials said.
Meanwhile, the officials said the mucormycosis (black fungus) tally remained unchanged at 46 in the union territory as no fresh case was reported.
On vaccination, the daily media bulletin on COVID-19 informs that 1,65,953 doses of COVID vaccine were administered in the last 24 hours bringing the cumulative number of doses administered across J&K to 12,353,745. It adds that 91.66 percent of the population above the age of 18 years has been vaccinated across Jammu and Kashmir so far.
According to the bulletin, out of 3,30,118 positive cases recorded cumulatively so far, currently there are 1,065 active positive cases across the UT including 786 in Kashmir and 279 in Jammu division, while as 3,24,627 persons have recovered and 4,426 have died – 2,252 in Kashmir and 2,174 in Jammu division.
Meanwhile India added 18,833 new coronavirus infections taking the country’s total tally of COVID-19 cases to 3,38,71,881, while the active cases declined to 2,46,687, the lowest in 203 days, according to the Union Health Ministry data updated on Wednesday.
The death toll climbed to 4,49,538 with 278 fresh fatalities, according to the data updated at 8 am.
The daily rise in new coronavirus infections remained below 30,000 for the 12th straight day.
The active cases comprise 0.73 percent of the total infections, the lowest since March 2020, while the national COVID-19 recovery rate was recorded at 97.94 percent, the highest since March 2020, the ministry said.
A decrease of 6,215 cases has been recorded in the active COVID-19 caseload in a span of 24 hours.
As many as 14,09,825 tests were conducted on Tuesday taking the total cumulative tests conducted so far for detection of COVID-19 in the country to 57,68,03,867.
The daily positivity rate was recorded at 1.34 percent. It has been less than three percent for last 37 days. The weekly positivity rate was recorded at 1.68 percent. It has been below three percent for the last 103 days, according to the ministry.
The number of people who have recuperated from the disease surged to 3,31, 75,656, while the case fatality rate was recorded at 1.33 percent.
The cumulative doses administered in the country so far under the nationwide COVID-19 vaccination drive has exceeded 92.17 crore.
India’s COVID-19 tally had crossed the 20-lakh mark on August 7, 2020, 30 lakh on August 23, 40 lakh on September 5 and 50 lakh on September 16. It went past 60 lakh on September 28, 70 lakh on October 11, crossed 80 lakh on October 29, 90 lakh on November 20 and surpassed the one-crore mark on December 19. India crossed two crore cases on May 04 and three crore cases on June 23.
The 278 new fatalities include 151 from Kerala and 39 from Maharashtra.
A total of 4,49,538 deaths have been reported so far in the country including 1,39,272 from Maharashtra, 37,845 from Karnataka, 35,682 from Tamil Nadu, 25,677 from Kerala and 25,088 from Delhi, 22,896 from Uttar Pradesh and 18,848 from West Bengal.
The ministry stressed that more than 70 percent of the deaths occurred due to comorbidities.
“Our figures are being reconciled with the Indian Council of Medical Research,” the ministry said on its website, adding that state-wise distribution of figures is subject to further verification and reconciliation .(With Inputs from PTI)


text:
Celiac disease, also known as gluten sensitive enteropathy is very common but frequently missed. It is an autoimmune disease of intestinal damage due to gluten in people who are genetically predisposed. Classic Celiac disease is diagnosed by abnormal blood tests and an abnormal appearing intestine on biopsy and symptoms that resolve with a gluten free diet.
Several blood tests exist for Celiac disease. They have varying degrees of accuracy. Some are more sensitive, meaning they will be positive in milder forms of the disease but are not specific, meaning a positive test may not indicate Celiac disease. Others are felt to be very specific, meaning that when they are positive, it is almost certain you have the disease.
The most specific tests are tests for Celiac disease endomysial antibodies (EMA) and tissue transglutaminase antibody (tTG) tests. These two tests are IgA based tests and can be negative if you are deficient in the immunoglobin IgA, which occurs in 10-20% of people with Celiac. When either EMA or tTG are positive Celiac disease is very likely and usually the intestine biopsy is positive. Recent studies indicate that the tTG may only be positive in 40% of true Celiacs when mild degrees of intestine damage are present on biopsy. Seronegative Celiac, meaning the blood tests are negative but the biopsy is positive, may occur in up to 20% of Celiacs.
Antibodies for gliadin (AGA), the toxic fraction of gluten are considered very sensitive but not specific for Celiac disease. Newer assays for AGA antibodies for gluten that has undergone a chemical change called deamidation appear to be more specific for Celiac disease (Gliadin II, Inova) than the older gliadin tests. They also may be as or more accurate than EMA and tTG antibody tests but are not yet widely available.
The most distressing problem for people with lesser forms of gluten intolerance who have blood tests and/or biopsies that are normal or borderline yet respond to a gluten free diet is either not being taken seriously or knowing for sure if they are sensitive to gluten. For these individuals stool antibody testing for antigliadin and tTG have been helpful. Such stool testing has been performed in research labs and published in a few studies but are only recently available through the commercial lab, Enterolab.
Founded by a former Baylor research gastroenterologist, Dr Ken Fine, the tests are available to people online without a doctors order but are not generally covered by insurance. Dr. Fine, who patented the test, has yet to publish the results of his findings in a peer reviewed journal so his tests are not widely accepted. However, his unpublished data and the clinical experience of some of us who have used his test have indicated the tests are very sensitive for signs of gluten sensitivity. He reports that they are 100% sensitive for Celiac disease and highly sensitive for gluten sensitivity of lesser degrees. In the presence of symptoms, that reverse on a gluten-free diet, abnormal stool antibody levels can be found in most people before blood tests or biopsies become abnormal.
Small intestine biopsies during upper gastrointestinal endoscopy are considered the “gold standard” for the diagnosis of Celiac disease. However, recent studies have demonstrated that some people with gluten sensitivity, especially relatives of Celiacs with little or no symptoms, have changes from gluten injury to the intestine that can not be seen with normal microscope examination. They can only be seen with special stains not routinely done or with a research electron microscope. The special stains are known as immunohistochemistry stains. They stain specialized white blood cells called lymphocytes in the intestinal lining tips or villi. When these lymphocytes are increased it is known as intraepithelial lymphocytosis or increased IELs and it is the earliest sign of gluten induced injury or irritation. Electron microscopy also reveals very early ultrastructural changes in some individuals when blood tests and standard biopsy examination are normal. When people who have these changes are offered the option of a gluten-free diet they usually responded favorably. In contrast, those who continue to eat gluten often later developed classic Celiac disease.
What these studies suggest is that a “normal small intestine biopsy” may exclude Celiac disease as defined by strict criteria but it is not a gold standard for detecting gluten sensitivity. This fact is appreciated by many individuals who have respond to a gluten-free diet they start based on their symptoms, family history, suggestive blood test or stool antibody test(s).
Another source of confusion is in the genetics of Celiac and gluten sensitivity. Testing for specific blood type patterns on white blood cells known as HLA DQ2 and DQ8 is increasingly being employed to determine if a person carries either of the two gene pattern present in 95-98% of Celiacs and predisposing them to the development of Celiac disease. Some use the absence of these two patterns as a way of excluding the possibility of Celiac disease and the need for testing or gluten-free diet. However, there are rare reports of documented Celiac disease in people who are DQ2 and DQ8 negative. Moreover, recent studies indicate other DQ patterns may be associated with gluten sensitivity though unlikely to predispose to classic Celiac disease.
Testing for all the DQ patterns is advocated by Dr. Fine, based on his experience with stool antibody test results. He reports that other DQ types are associated with elevated levels of gliadin and tTG in the stool and symptoms that respond to a gluten-free diet. According to his unpublished data, all the DQ types except DQ4 are associated with a risk of intolerance to gluten. Therefore, testing for all the DQ types allows a person to determine if they carry one of the two high risk gene types for Celiac disease or any of the other “minor DQ” genes Fine has found associated with gluten sensitivity.
Enterolab’s stool testing for gliadin antibodies and tissue transglutaminase antibodies, though not widely accepted, have gained favor in the lay public’s opinion as an option for determining sensitivity to gluten either despite negative blood tests and/or biopsies or in place of the more invasive tests. Most doctors still recommend the accepted blood tests and small bowel biopsy for confirmation of Celiac. Though the reports in the lay community are overwhelmingly positive they have not been subjected to peer review in the medical community pending Dr. Fine publishing his data or other researchers reproducing his results.
However, doctors open to the broader problem of gluten sensitivity are reporting these tests helpful in many patients suspected of gluten intolerance. Especially when someone has symptoms consistent with gluten sensitivity but has negative or inconclusive blood tests and/or biopsies these tests may be very helpful though some are not certain how to interpret the tests. The national Celiac organizations are uncertain about how to comment on their application without published research though a recent article in the British Medical Journal did show stool tests highly specific for Celiac. Dr. Fine has publicly commented that his unpublished data demonstrates those with abnormal stool tests indicating gluten sensitivity overwhelmingly respond favorably to a gluten free diet with improvement of symptoms and general quality of life.
Another problem is that there are not universally agreed upon definitions for gluten sensitivity or intolerance. This becomes especially difficult for those who do not meet strict criteria for Celiac disease yet may have abnormal tests and/or symptoms that respond to a gluten-free diet. Those individuals become confused when they try to find information but do not have a formal diagnosis of Celiac disease. Consensus in the medical community on definitions and more research in this area is greatly needed.
The few doctors who appreciate the spectrum of gluten intolerance or sensitivity are outnumbered by the medical majority that continue to insist on strict criteria for diagnosis for Celiac disease before recommending a gluten-free diet. Doctors either unfamiliar with the limitations of the tests as documented by Celiac research or who insist on the strict criteria for Celiac being the only indication for recommending a gluten free diet unfortunately may confuse or frustrate gluten sensitive individuals. Some of these people then seek answers on the internet or from alternative practitioners. Many have their diagnosis missed, challenged, dismissed, or are misinformed. As a result they fail to benefit from the health benefits of a gluten-free diet because they are advised that it is not required based on normal blood tests and/or normal biopsies.

domain: health


text:
We study the epidemiology of a viral disease with dose-dependent replication and transmission by nesting a differential-equation model of the within-host viral dynamics inside a between-host epidemiological model. We use two complementary approaches for nesting the models: an agent-based (AB) simulation and a mean-field approximation called the growth-matrix (GM) model. We find that although infection rates and predicted case loads are somewhat different between the AB and GM models, several epidemiological parameters, e.g. mean immunity in the population and mean dose received, behave similarly across the methods. Further, through a comparison of our dose-dependent replication model against two control models that uncouple dose-dependent replication from transmission, we find that host immunity in a population after an epidemic is qualitatively different than when transmission depends on time-varying viral abundances within hosts. These results show that within-host dynamics and viral dose should not be neglected in epidemiological models, and that the simpler GM approach to model nesting provides a reasonable tradeoff between model complexity and accuracy of results.
Standard epidemiological models of viral infections assume that each host is infected with the same amount of virus and experiences the same time course of infection. Yet, in natural epidemics, both disease time courses and doses of transmitted virus vary among hosts. Some authors have attempted to integrate viral dose into epidemiological models, finding that it can determine invasion success and persistence of viruses (Dushoff 1996; Regoes et al. 2002; Li & Wang 2009; Yu et al. 2009). For example, Dushoff (1996) studied a modified susceptible–infected–recovered (SIR) model in which there are two classes of infected individuals: one infected with a high viral dose and more severe infection, and the other with a low viral dose and more mild infection. This modified SIR model predicts that a virus can persist in a population by means of transmission through the hosts infected with a large dose even if the virus would not persist in a regular SIR model. Furthermore, whether or not an infection can spread in a population when viral dose is considered depends on a threshold viral abundance independent of R0, the average number of new infections caused by a single infected individual at the start of an epidemic (Regoes et al. 2002; Li & Wang 2009; Yu et al. 2009).
These results underscore that viral dose can determine epidemic dynamics. Yet, they have limited implications on understanding its impact on disease emergence, for two reasons. For one, dose is treated as a constant, which artificially amplifies its effects. Although constant dose size is an important extreme case to be considered for comparison, it is unlikely to occur commonly in nature. Secondly, in the epidemiological models of dose effects to date, dose-dependent dynamics within hosts are not considered. Therefore, these models cannot provide a mechanistic understanding of how viral dose impacts host immunity at the within-host and the population scale. To understand the role of dose- and time-varying viral dynamics on disease emergence risk, methods for analysing the interdependence of dose, within-host dynamics and transmission need to be developed.
The effect of viral dose on within-host dynamics in vivo is not completely understood, but some general principles have emerged. Viral dose impacts the viral growth curve as well as the strength of the host-immune response (Hughes et al. 2002; Quan et al. 2009). In viruses that cause acute infections, such as foot-and-mouth disease and influenza viruses, there is an inverse relationship between the size of viral dose and the infectious period, a critical determinant of transmission probability (Larson et al. 1976; Ottolini et al. 1996, 2005; Hughes et al. 2002; Alexandersen et al. 2003; Quan et al. 2004, 2009; Howey et al. 2008). Further, although the particular effects of viral dose on adaptive immunity levels are not completely understood, the data indicate that dose does in fact influence the dynamics of immunity, and that the relationship is complex and nonlinear. For example, the number of antibodies developed after clearing an infection increases with a very large viral dose (Ottolini et al. 1996; Hughes et al. 2002; Wherry et al. 2002), but a dose of moderate size tends to produce an amount of antibody comparable to that produced from a dose of small size. Thus, viral dose can influence both a central component of viral fitness, viral transmission and a major source of selection, host immunity.
Here, we develop two nested models (Mideo et al. 2008) for examining the effects of dose-dependent replication and transmission on epidemic behaviour. A single within-host model of viral and immunity dynamics is nested within two separate between-host models: an agent-based (AB) stochastic simulation model and a deterministic compartmental model that uses a next-generation matrix (GM) (Diekmann et al. 1990). These two between-host models differ in that the AB model is structured in the sense that each individual can only contact one other individual at each time step, whereas the GM model is mass-action, assuming an unhindered supply of connections between susceptible and infected individuals. A second difference is that the AB model allows for transmission during all possible states of the within-host dynamics, while the GM model coarse-grains within-host dynamics into discrete states that in turn determine transmission probability. To investigate whether the GM model is an accurate approximation to the more comprehensive AB model, we run simulations of epidemics under identical conditions for both models and compare model outputs. We show that the GM model is a reasonable approximation to the AB model. Further, through the use of two control models that remove dose-dependence on replication and transmission, we find that population immunity after an epidemic is qualitatively different when dose-dependent replication and transmission is considered.
2. Material and methods
(a) Within-host model
We consider a virus that causes an acute infection. The virus replicates with a rate r to an intrinsic carrying capacity KV and is removed by innate immunity I, non-specific memory cells N and specific memory cells P. Thus, we model three components of immunity based on the observed biology concerning acute viral infections such as influenza. Specifically, innate immune defences are the first to respond to viral infection, whereas non-specific memory cells begin being effective on the time scale of several days post infection (Hikono et al. 2006). With these principles in mind, we assume that I grows at a baseline rate of bI(1 − I/KI, where bI is a constant rate of growth and KI is the carrying capacity for I, and increases in proportion to viral abundance with rate constant aI. On the other hand, production of N is delayed and grows in proportion to viral abundance with rate constant aN. We model this delay using the Heaviside step function, Θ(t). Θ(t) = 0 for t < 0 and Θ(t) = 1 for t ≥ 0.
Specific memory cells arise from naive memory cells with a constant lag of τP and grow in proportion to the product of viral abundance and pre-existing P-cell abundance with rate constant aP, and in proportion to naive memory cells with rate constant c. N decays with rate constant dN, while P reaches an asymptote once N and V are cleared completely. We do not implement waning of specific memory cells since we do not consider reinfections here. Since the viral load does not drop until T cells are produced to fight the virus (Hikono et al. 2006), we assume that the innate immune response is less effective at lowering the viral titre than the T cell response, i.e. kI < kN < kP, where kI, kN and kP are the rate constants of removal of virus for I, N and P, respectively.
The equations governing the within-host dynamics are:
We chose the values of our parameters such that the relative values and the behaviour of our model conformed to the observed viral and immune dynamics. For example, the lag time for naive and specific immune cells has been set to 2.5 and 3 days, respectively, to fall in line with the observed progression of an immune response to an acute viral infection (Vermaelen et al. 2001; Legge & Braciale 2003; Lawrence & Braciale 2004; Hikono et al. 2006). We limit viral load to 1011 ml−1, which agrees with observed maximum viral loads (Baccam et al. 2006; Stephenson et al. 2009). We set kP to be considerably larger than either kI or kN to portray the superior efficacy of specific immune cells in clearing a viral infection (Cerwenka et al. 1999; Hikono et al. 2006). Note that effects of P on viral load are negligible in primary infections (P(0) = 0) because of the lag in P production, which captures the observed data (i.e. enhanced clearance from specific immunity in secondary infections but relatively little effect in primary infections). In primary infections, it is the levels of P that are produced from the viral dynamics that are the important focal point. The specific parameter settings for each simulation run are given in the captions of the corresponding figures.
(b) Between-host models
We modelled transmission between hosts using two approaches, both of which incorporate our within-host model into an SIR-type compartmental model of the host population. Our first transmission model, the growth-matrix (GM) model, is completely deterministic. It condenses the continuous-time within-host viral dynamics into a distribution of transmission events, thus approximating the effects of viral infection dynamics on transmission during an epidemic. Our second model is an AB simulation that tracks the state of infected hosts throughout their infectious period and modifies transmission according to viral load at contact. The contact structure of hosts in the AB model is such that at each time step of fixed size, Δt, every host is randomly matched pair-wise with another host. Thus, transmission in the AB model is stochastic because of contact among particular individuals, and the GM model is a deterministic approximation of the AB model.
In order to effectively compare predictions of the two models, we make the following common assumptions. First, both models assume mass-action dynamics, a constant population size (N = 1000) and no reinfection of recovered hosts. Second, we assume that bottleneck size at transmission is constant (10−4) such that the dose transmitted at any time t, is V(t) × 10−4. We also set the probability of transmission to 1 when the viral load of the infectious host is greater than a minimum threshold of infection (1 virion), so that an infection does not successfully grow in a host unless the dose is greater than or equal to one viral particle. Since transmission can occur at each epidemic time step in the AB model, we set the transmission rate, α (see below), equal to 1 in the GM model.
(c) Model 1: growth-matrix
To analyse the spread of virus through a host population that is heterogeneous with respect to within-host infection dynamics, we used a next-generation matrix (Diekmann et al. 1990), which operates in discrete time. The principle behind the next-generation matrix (G) is to divide the population into groups of individuals, classifying them by the dose received and, in the case of heterogeneous initial immunity, P(0) value. Given two classes of infected individuals, i and j, row i and column j of G represents the number of new infections of type i caused by one infection of type j in a single time step. We describe the potential doses and, in the case of heterogeneous initial immunity, P(0) values for infection of hosts by the vectors, ϕ(t) and ϕR(t), in which each entry describes the number of infected individuals who have received a certain dose with a given initial immunity, P(0).
(d) Uniform initial immunity
All individuals start out susceptible with P(0) equal to some constant value across the population. We introduce one individual infected by a specific viral dose. This host has the same P(0) as all other hosts in the population. ϕ is then a vector in which the number of entries equals the number of doses we consider, where ϕ(0) has all entries equal to 0 except for the entry corresponding to the dose in the initial host which has a value 1. Our growth matrix, G, is constructed by the following formula:
Here, β is the rate of transmission. Recall that we have set β = 1 in order to agree with transmission in the AB model. Tj is the time at which 10−4 V(t) drops below the viral dose size corresponding to i, given the within-host dynamics for a viral dose size corresponding to j. If 10−4 V(t) never reaches the viral dose size corresponding to i, then we set Gij equal to 0. Δt is the time step between updating the infection status of hosts and nj is an integer count of the number of time steps at which 10−4 V(t) is equal to the viral dose size corresponding to i.
In a similar spirit, we construct a recovery matrix, R, which describes the movement of infected individuals into the recovered class. From the within-host dynamics, we can determine a time, T′J, at which V(t) drops below 1 for a given viral dose J. We then have:
Here, k is the number of viral doses we consider.
Now, with a set population size, S(0), we can add Gϕ to ϕ, subtract Rϕ from ϕ, and add Rϕ to ϕR repeatedly in that order until the sum of the entries in ϕ and ϕR is either just below or equal to S(0). We then apply G once more, but scale the new inflow of infected individuals so that the sum of the entries in ϕ and ϕR equals S(0). In this way, the number of new cases will be Gϕ at each time step, and the last number of new cases will be a scaled value of Gϕ. At this point, we can subtract Rϕ from ϕ until the sum of the entries in ϕR reaches S(0) and the magnitude of ϕ drops below 1. The between-host dynamics are then given by the following equations:
Since we assume each individual that receives the same dose has the same course of infection, it is possible to calculate the mean immunity of newly recovered individuals simply by taking an average of the immune values for Rϕ at each time step. Likewise, we can calculate the mean dose at each time step by taking an average of the dose received in Gϕ at each time step. R0 is calculated as the ratio of the magnitudes of Gϕ and ϕ. This calculation of R0 does not give the total number of new cases caused by an infected individual during the epidemic, but instead it produces a mean number of new cases in one time step caused by an infected individual.
(e) Heterogeneous initial immunity
To consider heterogeneous P(0) in a population, we consider classes of infected individuals based on both the viral dose received and the value of P(0). Thus, for m levels of initial immunity and k viral doses, ϕ will have mk entries. To create a growth matrix for this situation, we assume that the number of new infections of a given dose caused by one individual can be divided evenly among the infected classes with any P(0) and that given dose. In other words,
Once again, we create a recovery matrix that is a diagonal matrix, such that the ith entry is the rate of recovery for the ith class.
In order to analysze infection spread in this case for a given initial dose, we run the same method described above for an initial host with 10 different values of P(0) equally spaced in [0, 2] and the V(0) considered. Epidemiological parameters are calculated in the same fashion as described above.
(f) Agent-based model
We use an AB simulation model to examine whether condensing the within-host dynamics via the next-generation matrix approach is similar to epidemiological dynamics determined by the entire viral growth curve and random host contact. Each simulation begins with a single infected host. Ten replicate simulations are run for each set of initial viral dose, V(0), and specific immunity level, P(0). Contact between hosts occurs in discrete time. At each time step, half of the host population is randomly matched with the other half of the population (i.e. random contact of the entire population). The hosts are organized pair-wise such that each host has the opportunity to transmit the virus to exactly one other host. Transmission occurs only from an infected host to a susceptible host. For the case of heterogeneous initial immunity in the population, we assign a random P(0) value in [0, 2] to each host upon infection.
The number of infections caused during the infectious period of each host, R0, is recorded at recovery. Mean R0 of recovering hosts is calculated at each time step. Likewise, mean dose in newly infected hosts, total new cases and mean peak-specific immunity levels in recovering hosts are calculated at each epidemic time step.
(a) Within-host model
The within-host model describes dose-dependent replication and immune response. Figure 1 shows the combined effects of viral dose and initial specific immunity level, P(0), on within-host behaviour. The maximum viral load and level of newly acquired specific immunity increases with viral dose for a given level of P(0) (figure 1a,b). On the other hand, both infectious period and time of maximum viral load decrease with viral dose (figure 1c,d).
A noteworthy aspect of our within-host system is the nonlinear relationship between P(0) and the strength of dose-dependent effects on replication dynamics. For example, both peak viral load and infectious period depend more strongly on viral dose at the extreme values of P(0), i.e. P(0) = 0 and 2, whereas only very small dose-dependent effects occur at moderate levels of P(0), i.e. P(0) = 0.5 and 1 (figure 1a,d). Similarly, P(0) has a different functional relationship to viral dose at moderate levels of P(0) relative to the extreme values, albeit generally producing the shortest time to peak viral load and highest level of peak immunity at moderate level of P(0) (figure 1b,c).
(b) Population dynamics
In order to determine effects of within-host dynamics on epidemic behaviour in a population comprised entirely of susceptible hosts, we ran population level simulations using both the GM and AB models of transmission for a host population with no specific immunity (i.e. P(0) = 0 for all hosts). Owing to inherent differences in the approaches, there are some discrepancies between predictions from the two models. For example, the GM model predicts faster, more severe epidemics relative to the AB model, because of a methodological difference in the implementation of host contact. In the GM model, the infectious class has a constant supply of connections to susceptibles, whereas the AB model uses an explicit contact structure, which limits the rate of transmission since each host is connected pair-wise to only a single other host at each time step.
Despite this methodological difference in modelling contact, the models produce qualitatively similar and typical epidemic behaviour: initial exponential growth of new cases followed by a decline to extinction as the susceptible pool is depleted (figure 2a,b). Neither model shows a dependence of epidemic dynamics on dose in the first host, although the models do predict qualitatively similar effects of dose-dependent replication and transmission on mean immunity and dose trajectories. Mean immunity of recovered individuals starts out high but drops as the epidemic progresses (figure 2c,d). This decline corresponds to a decline in viral dose, which emerges as susceptible hosts become rare. Mean dose increases rapidly at the outset but then remains roughly constant throughout most of the epidemic, also until susceptible hosts become rare (figure 2e,f).
By contrast, the models predict slightly different trends in R0, which is partly because of the different methods used for estimating R0 (figure 2g,h). While the AB model produces R0 based on the entire infection course of individual hosts that recover at a particular time step, the GM model produces an R0 based on the new cases produced in one time step. For this reason, the R0 produced by the GM model is lower. Further, a gradual decline in R0 occurs with the AB model but not in the GM model, a disagreement that can be attributed to the fact that the AB model captures the loss of frequent contact between susceptible and infected individuals as susceptibles become more rare, while the GM model does not.
(c) Heterogeneous P(0)
In reality, the distribution of initial immunity in a population is almost always heterogeneous. For this reason, we ran simulations under heterogeneous population P(0). For the GM model, we introduced a new dimension to the classes of infected individuals in the form of a discrete number of P(0) values. For the AB model, P(0) for each host in the population was drawn randomly from a uniform distribution on the interval [0, 2]. For the GM model, the runs consisted of varying the initial host such that the dose remains constant, but P(0) was set to each of 10 levels considered. We assumed that P(0) in the population was uniformly distributed in the range of 10 values mentioned previously. For the AB model, the runs consisted of a constant viral dose in the initial host and a random initial immunity in the introductory host.
The models show qualitative agreement in their mean behaviour. Specifically, all replicate runs for both models reach a common equilibrium in mean immunity, mean dose and R0 (figure 3). This emphasizes that epidemiological behaviour depends on the within-host dynamics in currently infected individuals (rather than those of the introductory host), which is a common feature of our models. We also see that the mean immunity of recovered individuals gradually declines in both models (figure 3c,d), a result we noted also with uniform initial immunity in the population.
Both the number of new cases and the mean dose are notably higher in the GM model, and this can be attributed to the method used for modelling heterogeneous P(0) with the GM model. We assumed that upon infection, an individual will generate an equal number of new infections with a given dose size for each level of initial P(0) in the new host. Thus, completely susceptible individuals are always infected and are able to drive up the number of new cases and also the mean dose. Since new infections in the AB model have a stochastic element in the initial P(0) value, this effect from completely susceptible individuals is not present in the way it is for the GM model.
(d) Effects of viral dose on epidemic behaviour
Next, we examined the effects of viral dose and initial immunity on epidemic behaviour and the profile of host population immunity via the following characteristics: (i) epidemic severity: the peak number of new cases; (ii) epidemic rate: the time to the peak number of cases (figure 4); (iii) mean population immunity: mean immunity in the host population after the epidemic; and (iv) variability in immunity: the coefficient of variation in this mean (figure 5). We note that the dose in the initial host does not have much effect in this analysis, except under heterogeneous P(0) in the AB model and P(0) = 2 in both the models. For heterogeneous P(0) in the AB model, we see that for a dose of 1 virion in the initial host, it is very likely that the epidemic will not spread. Similarly, for P(0) = 2 in both models, a large viral dose in the initial host is necessary for epidemic transmission.
The effects of initial immunity on epidemic severity and rate reveal some disagreement between the AB and GM models. First, in the AB model, epidemic severity is sensitive to moderate increases in P(0), whereas the GM model predicts similar case rates across low to moderate P(0) (figure 4a,b). Furthermore, at high P(0), a positive correlation of dose to epidemic severity and rates emerges in the GM model but not the AB model. Again, these differences can be attributed to the different methods of host contact (addressed above), which underscores the importance of host contact structure in quantitative predictions of caseloads. In contrast, epidemic rates are similarly negatively correlated to P(0) at 0 and moderate levels of P(0) (figure 4c,d), although the relative difference in rates is slightly more pronounced for the AB model relative to the GM model.
In both models, mean immunity in the population after the epidemic is lower when P(0) is higher, although again, the AB model predicts stronger effects. Interestingly, heterogeneity creates slightly higher population immunity relative to that resulting from a homogeneous population with P(0) equal to 1 (i.e. the mean of the heterogeneous case) (figure 5a,b). Further, note that a population with high partial immunity (P(0) = 2) has the same level of immunity as when the epidemic started, which is a result of the fact that in both models, the epidemic is very mild for all initial doses. The variance in immunity after an epidemic is generally lower in the GM model, but we do see agreement in the result that a population with heterogeneous P(0) will emerge from the epidemic with the largest amount of variation, a result that seems intuitive (figure 5c,d). However, less intuitive is the discrepancy between models in the levels of heterogeneity produced by different P(0) values: the AB model predicts that moderate P(0) will result in much higher levels of population variability in immunity relative to the levels emerging from a completely naive population, whereas the GM model predicts similarly low levels of variability in both cases. We attribute this effect to the contact structure of the AB model, which causes a significant proportion of a P(0) = 1 population never to become infected. These individuals who do not become infected increase the range of maximum P-values (figure 1b), resulting in larger variability in mean P than for the GM model (which sees no proportion of the P(0) = 1 population left uninfected). This increase in variability caused by a fraction of the population not being infected in the AB model is also evident in the heterogeneous P(0) case.
(e) Control models
To compare the effect of dose-dependent replication and transmission against a system without such an effect, we created two models in which transmission was uncoupled from dose-dependent replication. In the constant-dose (CD) control model, the same dose is used to initiate all infections throughout the epidemic, whereas in the random-dose (RD) control model, the viral dose is random at every transmission, regardless of the viral load in the infected host. The control models give us a picture of the epidemic for two cases: one in which the within-host dynamics are constant and transmission is dose-independent, similar to a standard three-compartment SIR model, where dynamics of infected individuals depend on contact rates and infectious period, and the other in which the within-host dynamics are variable, as in our model, and transmission is dose-independent, which is similar to epidemiological models that include stochastic transmission (i.e. from random noise, not stochasticity from viral dose). Unlike the dose-dependent models, in the control models, the epidemic always progresses regardless of the value of P(0), since transmission does not involve a bottleneck (i.e. infected hosts always transmit to susceptible hosts) (electronic supplementary material, figures S1, and S3).
In the CD model, there is a slight decrease in epidemic severity and increase in epidemic rate with increasing dose (electronic supplementary material, figure S1). Note that an effect of dose emerges in this model because every infection is initiated with the same dose as the initial host received. Since a smaller dose results in a longer infection period, the maximum number of cases is larger and the time to reach this maximum is shorter. This effect is similar to that found in more standard epidemiological models that base transitions between compartments on infectious period. However, in the RD model, epidemic severity and rate are relatively insensitive to initial dose since the mean dose is constant across all initial dose conditions (electronic supplementary material, figure S3).
Across the control and dose-dependent models, we find that variation in immunity is highest for a heterogeneous P(0) population (electronic supplementary material, figures S2 and S4). Similar to the dose-dependent models, variation in immunity is increased for the AB model when not all susceptibles are infected in a P(0) = 1 or 2 population. The main difference between the control models and dose-dependent models is that variation in immunity changes with P(0) more in the dose-dependent model, emphasizing that dose-dependent replication and transmission enhances the effect of initial immunity in a population on immunity variation in the population after an epidemic.
There is also a significant qualitative difference between the control models and the dose-dependent model predictions of population immunity following the epidemic. In the dose-dependent model, the magnitude of effects of P(0) on epidemic behaviour is much higher than in the two control models (electronic supplementary material, figures S2 and S4). There is also a significant qualitative difference between the control models and the dose-dependent model predictions of mean population immunity following the epidemic. In the control models, mean immunity in the population is highest for moderate P(0) = 1, and then decreases for P(0) = 0 and P(0) = 2. On the other hand, in the dose-dependent model, we see that the order is P(0) = 0, P(0) = 1 and then P(0) = 2. Thus, when dose-dependent replication determines transmission, mean immunity in a population is highest in a completely naive population, whereas when transmission is uncoupled from dose-dependent within-host dynamics, mean immunity is highest for a population with a moderate initial immunity. Together, these differences indicate that dose-dependent within-host dynamics can affect host population immunity both quantitatively and qualitatively.
We have compared two methods of nesting a dose-dependent within-host model of viral dynamics into an epidemiological model of a host population. The AB model provides a more complete integration of dynamics across these scales at the expense of computational intensity, whereas the GM model sacrifices some of the time-dependent quantities for computational ease. In analysing the effect of dose-dependent replication and transmission on population-level viral dynamics, we found that the GM model provides a reliable approximation of mean peak immunity in recovered hosts and viral doses throughout the epidemic time course. We also found that effects of initial immunity on the profile of population immunity after the epidemic are qualitatively different when dose-dependent replication and transmission dynamics are considered.
The GM model served as a mean-field approximation to the stochastic transmission in the AB model, leaving all common parameters equal. For this reason, the GM model is able to provide us similar insight to the behaviour of an epidemic in a population with varying levels of initial uniform immunity. Previous comparisons between deterministic compartmental models and AB models have shown that while AB models can provide a more realistic representation of epidemic dynamics because of their ability to capture more biological complexity and stochasticity, deterministic compartmental models can provide a reliable estimation of the mean behaviour (Brailsford & Hilton 2000; Schieritz & Milling 2003; Borschev & Filippov 2004; Morecroft & Robinson 2005; Demirel 2006; Rahmandad & Sterman 2008). This initial study revealed some method-based inconsistencies in predicted epidemic behaviour, e.g. epidemic rate and severity. Nevertheless, there are some strikingly congruent predictions across the models, and the GM model provides a promising foundation for the development of more complex analytical models that can examine long-term effects of specific immunity through reinfection of recovered individuals, as well as the evolution of immune escape.
Through comparison of the effect of initial population immunity on post-epidemic population immunity in the dose-dependent model and the two control models, we uncovered a qualitative disagreement between a system with dose-dependent transmission and systems with dose-independent transmission. The idea that levels of population immunity can be hindered by pre-existing immunity has been proposed for influenza epidemiology (Smith et al. 1999). This emphasizes the need for continued development of methods for understanding how within-host dynamics modify immunity in heterogeneous host populations, in order to inform disease control policies, such as identifying which groups should be given priority for vaccination. While several theoretical studies have addressed these issues using compartmental epidemiological models (Hethcote & VanArk 1987; Handeler & Muller 1996; Castillo-Chavez & Feng 1998; Hill & Longini 2003; Tennenbaum 2008), the impact of within-host dynamics on population immunity is yet to be considered in this context. Future work aimed at understanding how infection-induced variability in immunity levels impacts viral immune escape will help to understand how disease control methods can be implemented with the lowest risk of viral emergence.
In addition to within-host dynamics, host contact-network structure is a major determinant of between-host dynamics. In scale-free networks (i.e. the degree of connectivity between individuals is characterized by a power law distribution), epidemic spread is hierarchial, erupting first in the most connected individuals and last in the least connected ones (Barthélemy et al. 2004, 2005). Secondly, models that include this type of finite-network structure fail to exhibit epidemic threshold conditions that are typically observed in epidemiological models of finite populations (May & Lloyd 2001; Pastor-Satorras & Vespignani 2001). Future research should aim to integrate the effects of time-varying viral dynamics on transmission with those from network-constrained epidemic spread in order to predict how viral transmission is concurrently guided by within- and between-host factors. This type of integrative framework will reveal the constraints on viral emergence that can be used as a premise for developing effective methods of prevention.
Our calculation of R0 differed across the models. R0 represents the total number of new cases caused by a single infectious individual during an epidemic, and for this reason, the AB calculation of R0 is closer to that which could be calculated from real-world case data. However, the GM model does not allow for a calculation of this sort. Thus, as it stands, estimates of R0 using the GM model may not compare well with estimations from real-world data.
Most of the parameter values in our models were chosen based on observed data of acute viral infections, but some of the quantities are either not known or have been described only to a limited degree (e.g. quantitative details of the functional relationship of viral dynamics and specific immunity in primary versus secondary infections). Thus, we do not have an experimentally validated system, and this is an aspect of the research we hope can be improved in future work through collaboration with virologists and immunologists. Since we are mainly interested in model comparison in this work, however, we do not believe that this weakness affects our main finding that dose-dependent replication and transmission is an important driver of epidemiological dynamics, particularly the changing profile of host population immunity.
In future studies we plan to build on this framework by including mutation (i.e. multiple strains) to examine how these effects from dose-dependent within-host dynamics determine the emergence of immune-escape strains in a host population with changing immunity. Understanding the effects of dose-dependent within-host kinetics on mutation frequency and immune response is also important for developing viral disease therapies (e.g. drugs that impact viral kinetics) that optimize therapeutic gain with minimal risk of immune-escape evolution. For example, large doses would contain more viral mutants than smaller doses at each transmission event, which could result in more heterogeneity in specific immune responses within hosts in the population, but lower variation between hosts.
In conclusion, we have found that a mean-field approximation to discrete transmission events, the GM model, provides a reliable approximation for epidemic behaviour in terms of mean immunity. We have also found that dose-dependent replication and transmission have an effect on population immunity after an epidemic, in contrast to a dose-independent transmission model.
This work was supported by NSF grant EF-0742373. K.M.P. was also supported by NIH grant R01 GM083983-01.
One contribution of 14 to a Theme Issue ‘New experimental and theoretical approaches towards the understanding of the emergence of viral infections’.
- © 2010 The Royal Society


text:
With production expected to begin later this year and sales likely to commence locally in 2017, the next-generation A5 family is not far from showrooms.
As previously reported, the new A5's look won't be a radical departure from the current model, being more of an evolution rather than revolution.
The Sportback's design closely follows that of its coupe and cabriolet siblings, with a more squared-off front grille and more distinguished tail-light signature than the current model.
The new A5 family continues the manufacturer's latest evolution in its design language, already previewed by the latest TT sports car, Q7 SUV and the recently launched A4 sedan and wagon.
The biggest changes will be made under the skin, with the next A5 sharing the new Volkswagen Group MLB platform - which already underpins the new A4 and Q7 stablemates, which should help the next-generation A5 shed about 100 kilograms.
The A5 Sportback's engine line-up will most likely mirror the coupe and cabriolet variants, with a range of turbocharged petrol and diesel engines almost certain to feature, along with front and Quattro all-wheel drive drivetrain options.
Stay tuned for more information on the 2017 Audi A5 in the coming months.
MORE: Audi A5 news and reviews