Estimating Time of Infection Using Prior Serological and Individual Information Can Greatly Improve Incidence Estimation of Human and Wildlife Infections

PLoS Computational Biology

Volumn 12, Issue 5, 2016, Pages

  Borremans, Benny ^a   Hens, Niel ^a,b   Beutels, Philippe ^a   Leirs, Herwig ^a   Reijniers, Jonas ^a  

^a UNIVERSITY OF ANTWERP   (Belgium)

^b HASSELT UNIVERSITY   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMALS; INFORMATION USE; PATHOGENS;

ASYMPTOMATIC INFECTION; BAYESIAN FRAMEWORKS; LARGE ESTIMATION ERRORS; MULTIPLE SOURCE; SAMPLE SIZES; SAMPLING INTERVAL; SEMI-PARAMETRIC MODELING; SOURCES OF INFORMATIONS; TEMPORAL PATTERN; TIME UNITS;

ANTIBODIES;

AGE; ANIMAL EXPERIMENT; ANIMAL MODEL; ANTIBODY BLOOD LEVEL; ARENAVIRUS INFECTION; ARTICLE; BACTERIUM; BAYES THEOREM; BLOOD; CONTROLLED STUDY; DISEASE MODEL; ENTROPY; EXCRETION; HUMAN; INCIDENCE; INCUBATION TIME; INFORMATION; LIFESPAN; MOROGORO VIRUS INFECTION; MORTALITY RATE; MOUSE; NONHUMAN; PROBABILITY; SEASON; SEROCONVERSION; SEROLOGY; VIRUS; ANIMAL; BIOLOGICAL MODEL; BIOLOGY; COMPUTER SIMULATION; IMMUNOLOGY; INFECTION; STATISTICAL MODEL; TIME FACTOR; VETERINARY; WILD ANIMAL;

ANIMALS; ANIMALS, WILD; BAYES THEOREM; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; HUMANS; INCIDENCE; INFECTION; MICE; MODELS, BIOLOGICAL; MODELS, STATISTICAL; SEROCONVERSION; TIME FACTORS;

EID: 84975794157     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004882     Document Type: Article

References (39)

1. Khan AS, Tshioko FK, Heymann DL, Le Guenno B, Nabeth P, et al. (1999) The reemergence of Ebola hemorrhagic fever, Democratic Republic of the Congo, 1995. Commission de Lutte contre les Epidémies à Kikwit. J Infect Dis179: S76–S86. 9988168
2. Brookmeyer R, (2010) Measuring the HIV/AIDS epidemic: approaches and challenges. Epidemiol Rev32: 26–37. doi: 10.1093/epirev/mxq00220203104
3. Richardson Ba, Hughes JP, (2000) Product limit estimation for infectious disease data when the diagnostic test for the outcome is measured with uncertainty. Biostatistics1: 341–354. doi: 10.1093/biostatistics/1.3.34112933514
4. Sal y Rosas VG, Hughes JP, (2011) Nonparametric and semiparametric analysis of current status data subject to outcome misclassification. Stat Commun Infect Dis3: 7.
5. Begon M, Feore S, Bown KJ, Chantrey J, Jones T, et al. (1998) Population and transmission dynamics of cowpox in bank voles: testing fundamental assumptions. Ecol Lett1: 82–86. doi: 10.1046/j.1461-0248.1998.00018.x
6. Hangartner L, Zinkernagel RM, Hengartner H, (2006) Antiviral antibody responses: the two extremes of a wide spectrum. Nat Rev Immunol6: 231–43. doi: 10.1038/nri178316498452
7. Best JM, Banatvala JE, Watson D, (1969) Serum IgM and IgG responses in postnatally acquired rubella. Lancet2: 65–68. doi: 10.1016/S0140-6736(69)92386-14182759
8. Uhr JW, Finkelstein MS, (1963) Antibody formation. IV. Formation of rapidly and slowly sedimenting antibodies and immunological memory to bacteriophage phi-X 174. J Exp Med117: 457–477. doi: 10.1084/jem.117.3.45713995245
9. Schoppel K, Kropff B, Schmidt C, Vornhagen R, Mach M, (1997) The humoral immune response against human cytomegalovirus is characterized by a delayed synthesis of glycoprotein-specific antibodies. J Infect Dis175: 533–544. doi: 10.1093/infdis/175.3.5339041323
10. Baccard-Longere M, Freymuth F, Cointe D, Seigneurin JM, Grangeot-keros L, (2001) Multicenter evaluation of a rapid and convenient method for determination of Cytomegalovirus immunoglobulin G avidity. Clin Diagn Lab Immunol8: 429–431. doi: 10.1128/CDLI.8.2.429-431.200111238233
11. Gray JJ, Cohen BJ, Desselberger U, (1993) Detection of human parvovirus Bl9-specific IgM and IgG antibodies using a recombinant viral VP1 antigen expressed in insect cells and estimation of time of infection by testing for antibody avidity. J Virol Methods44: 11–23. doi: 10.1016/0166-0934(93)90003-A8227275
12. Revello MG, Genini E, Gorini G, Klersy C, Piralla A, et al. (2010) Comparative evaluation of eight commercial human cytomegalovirus IgG avidity assays. J Clin Virol48: 255–259. doi: 10.1016/j.jcv.2010.05.00420561816
13. Versteegh FGA, Mertens PLJM, de Melker HE, Roord JJ, Schellekens JFP, et al. (2005) Age-specific long-term course of IgG antibodies to pertussis toxin after symptomatic infection with Bordetella pertussis. Epidemiol Infect133: 737–748. doi: 10.1017/S095026880500383316050521
14. de Melker HE, Versteegh FGA, Schellekens JFP, Teunis PFM, Kretzschmar M, (2006) The incidence of Bordetella pertussis infections estimated in the population from a combination of serological surveys. J Infect53: 106–113. doi: 10.1016/j.jinf.2005.10.02016352342
15. de Angelis D, Gilks W, Day N, (1998) Bayesian projection of the acquired immune deficiency syndrome epidemic. J R Stat Soc Ser C Applied Stat47: 449–498. doi: 10.1111/1467-9876.00123
16. Heisterkamp SH, de Vries R, Sprenger HG, Hubben GAA, Postma MJ, (2008) Estimation and prediction of the HIV-AIDS-epidemic under conditions of HAART using mixtures of incubation time distributions. Stat Med27: 781–794. doi: 10.1002/sim.297417597471
17. Simonsen J, Mølbak K, Falkenhorst G, Krogfelt K, Linneberg A, et al. (2009) Estimation of incidences of infectious diseases based on antibody measurements. Stat Med28: 1882–1895. doi: 10.1002/sim.359219387977
18. Teunis PFM, van Eijkeren JCH, Ang CW, van Duynhoven YTHP, Simonsen JB, et al. (2012) Biomarker dynamics: estimating infection rates from serological data. Stat Med31: 2240–2248. doi: 10.1002/sim.532222419564
19. Miller FG, Grady C, (2001) The ethical challenge of infection-inducing challenge experiments. Clin Infect Dis33: 1028–1033. doi: 10.1086/32266411528576
20. Marty AM, Jahrling PB, Geisbert TW, (2006) Viral hemorrhagic fevers. Clin Lab Med26: 345–386. doi: 10.1016/j.cll.2006.05.00116815457
21. Borremans B, Leirs H, Gryseels S, Günther S, Makundi R, et al. (2011) Presence of Mopeia virus, an African arenavirus, related to biotope and individual rodent host characteristics: implications for virus transmission. Vector-Borne Zoonotic Dis11: 1125–1131. doi: 10.1089/vbz.2010.001021142956
22. Leirs H, Stenseth NC, Nichols JD, Hines JE, Verhagen R, et al. (1997) Stochastic seasonality and nonlinear density-dependent factors regulate population size in an African rodent. Nature389: 176–180. doi: 10.1038/382719296494
23. Borremans B, Vossen R, Becker-Ziaja B, Gryseels S, Hughes N, et al. (2015) Shedding dynamics of Morogoro virus, an African arenavirus closely related to Lassa virus, in its natural reservoir host Mastomys natalensis. Sci Rep5: 10445. doi: 10.1038/srep1044526022445
24. Andraud M, Lejeune O, Musoro JZ, Ogunjuni B, Beutels P, et al. (2012) Living on three time scales: the dynamics of plasma cell and antibody populations illustrated for hepatitis A virus. PLoS Comput Biol8: e1002418. doi: 10.1371/journal.pcbi.100241822396639
25. Hens N, Shkedy Z, Aerts M, Faes C, Van Damme P, et al. (2012) Modeling infectious disease parameters based on serological and social contact data. A modern statistical perspective. Springer.
26. Bollaerts K, Aerts M, Shkedy Z, Faes C, Van der Stede Y, et al. (2012) Estimating the population prevalence and force of infection directly from antibody titres. Stat Modelling12: 441–462. doi: 10.1177/1471082X12457495
27. Pollock KH, Nichols JD, Brownie C, Hines JE, (1990) Statistical inference for capture-recapture experiments. Wildl Monogr107: 3–97.
28. White GC, Anderson DR, Burnham KP, Otis DL, (1982) Capture-recapture and removal methods for sampling closed populations. Los Alamos, New Mexico: Los Alamos National Laboratory.
29. Cover TM, Thomas JA, (1991) Elements of information theory. New York: John Wiley & Sons.
30. Goyens J, Reijniers J, Borremans B, Leirs H, (2013) Density thresholds for Mopeia virus invasion and persistence in its host Mastomys natalensis. J Theor Biol317: 55–61. doi: 10.1016/j.jtbi.2012.09.03923041432
31. Leirs H, Verhagen R, Verheyen W, (1993) Productivity of different generations in a population of Mastomys natalensis rats in Tanzania. Oikos68: 53–60. doi: 10.2307/3545308
32. Childs JE, Peters CJ, Salvato MS, (1993) Ecology and epidemiology of arenaviruses and their hosts. In: The Arenaviridae, New York: Springer US. pp. 331–384. doi: 10.1007/978-1-4615-3028-2_19
33. Simonsen J, Strid M, Mølbak K, Krogfelt K, Linneberg A, et al. (2008) Sero-epidemiology as a tool to study the incidence of Salmonella infections in humans. Epidemiol Infect136: 895–902. doi: 10.1017/S095026880700931417678562
34. Smith MJ, Telfer S, Kallio ER, Burthe S, Cook AR, et al. (2009) Host-pathogen time series data in wildlife support a transmission function between density and frequency dependence. Proc Natl Acad Sci106: 7905–7909. doi: 10.1073/pnas.080914510619416827
35. Tersago K, Verhagen R, Leirs H, (2010) Temporal variation in individual factors associated with Hantavirus infection in bank voles during an epizootic: implications for Puumala virus transmission dynamics. Vector-Borne Zoonotic Dis11: 715–721. doi: 10.1089/vbz.2010.000721142469
36. Sluydts V, Davis S, Mercelis S, Leirs H, (2009) Comparison of multimammate mouse (Mastomys natalensis) demography in monoculture and mosaic agricultural habitat: Implications for pest management. Crop Prot28: 647–654. doi: 10.1016/j.cropro.2009.03.018
37. Borremans B, Hughes NK, Reijniers J, Sluydts V, Katakweba AAS, et al. (2014) Happily together forever: temporal variation in spatial patterns and complete lack of territoriality in a promiscuous rodent. Popul Ecol56: 109–118. doi: 10.1007/s10144-013-0393-2
38. Bernshtein AD, Apekina NS, Mikhailova TV, Myasnikov Y, Khlyap L, et al. (1999) Dynamics of Puumala hantavirus infection in naturally infected bank voles (Clethrinomys glareolus). Arch Virol144: 2415–2428. doi: 10.1007/s00705005065410664394
39. Begon M, Hazel S, Telfer S, Bown K, Carslake D, et al. (2003) Rodents, cowpox virus and islands: densities, numbers and thresholds. J Anim Ecol72: 343–355. doi: 10.1046/j.1365-2656.2003.00705.x