TY - JOUR
T1 - Bayesian estimation of lassa virus epidemiological parameters
T2 - Implications for spillover prevention using wildlife vaccination
AU - Nuismer, Scott L.
AU - Remien, Christopher H.
AU - Basinski, Andrew J.
AU - Varrelman, Tanner
AU - Layman, Nathan
AU - Rosenke, Kyle
AU - Bird, Brian
AU - Jarvis, Michael
AU - Barry, Peter
AU - Hanley, Patrick W.
AU - Fichet-Calvet, Elisabeth
PY - 2020/9
Y1 - 2020/9
N2 - Lassa virus is a significant burden on human health throughout its endemic region in West Africa, with most human infections the result of spillover from the primary rodent reservoir of the virus, the natal multimammate mouse, M. natalensis. Here we develop a Bayesian methodology for estimating epidemiological parameters of Lassa virus within its rodent reservoir and for generating probabilistic predictions for the efficacy of rodent vaccination programs. Our approach uses Approximate Bayesian Computation (ABC) to integrate mechanistic mathematical models, remotely-sensed precipitation data, and Lassa virus surveillance data from rodent populations. Using simulated data, we show that our method accurately estimates key model parameters, even when surveillance data are available from only a rel-atively small number of points in space and time. Applying our method to previously pub-lished data from two villages in Guinea estimates the time-averaged R0 of Lassa virus to be 1.74 and 1.54 for rodent populations in the villages of Bantou and Tanganya, respectively. Using the posterior distribution for model parameters derived from these Guinean popula-tions, we evaluate the likely efficacy of vaccination programs relying on distribution of vac-cine-laced baits. Our results demonstrate that effective and durable reductions in the risk of Lassa virus spillover into the human population will require repeated distribution of large quantities of vaccine.
AB - Lassa virus is a significant burden on human health throughout its endemic region in West Africa, with most human infections the result of spillover from the primary rodent reservoir of the virus, the natal multimammate mouse, M. natalensis. Here we develop a Bayesian methodology for estimating epidemiological parameters of Lassa virus within its rodent reservoir and for generating probabilistic predictions for the efficacy of rodent vaccination programs. Our approach uses Approximate Bayesian Computation (ABC) to integrate mechanistic mathematical models, remotely-sensed precipitation data, and Lassa virus surveillance data from rodent populations. Using simulated data, we show that our method accurately estimates key model parameters, even when surveillance data are available from only a rel-atively small number of points in space and time. Applying our method to previously pub-lished data from two villages in Guinea estimates the time-averaged R0 of Lassa virus to be 1.74 and 1.54 for rodent populations in the villages of Bantou and Tanganya, respectively. Using the posterior distribution for model parameters derived from these Guinean popula-tions, we evaluate the likely efficacy of vaccination programs relying on distribution of vac-cine-laced baits. Our results demonstrate that effective and durable reductions in the risk of Lassa virus spillover into the human population will require repeated distribution of large quantities of vaccine.
UR - http://www.scopus.com/inward/record.url?scp=85092214578&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092214578&partnerID=8YFLogxK
U2 - 10.1371/journal.pntd.0007920
DO - 10.1371/journal.pntd.0007920
M3 - Article
C2 - 32956349
AN - SCOPUS:85092214578
VL - 14
SP - 1
EP - 20
JO - PLoS Neglected Tropical Diseases
JF - PLoS Neglected Tropical Diseases
SN - 1935-2727
IS - 9
M1 - e0007920
ER -