Abstract:Usutu virus (USUV) is a zoonotic mosquito-borne virus that can cause neuroinvasive disease, including meningitis and encephalitis, in humans and has resulted in hundreds of thousands of deaths in wild birds. The perpetuation of USUV in nature is dependent on transmission between
Culex
spp. mosquitoes and various avian species.
“…Understanding the epidemiology of emerging pathogens, such as USUV, requires systems investigation at each scale involved in the host-virus transmission cycle, from individual infection in birds, to bird-tovector transmission, to USUV incidence in bird and vector populations and eventually to spillover probability in humans [37][38][39]. For new pathogens, field data is sparse, and predictions are based on laboratory-type inoculation and transmission experiments [9,11] combined with dynamical mathematical modelling [9].…”
Section: Discussionmentioning
confidence: 99%
“…Note that the effect of viral loss through cell entry, given by − βTV , is negligible compared with virus production and clearance terms and, therefore, not included in the virus equation. We aim to estimate model equation (2.1)’s parameters by fitting it to published infectious virus titres in wild-caught house sparrows infected with either Netherlands 2016 or Uganda 2012 [11]. Figure 1Model diagram describing the within-host USUV infection.…”
Section: Methodsmentioning
confidence: 99%
“…Culex quinquefasciatus mosquitoes were fed on birds infected with USUV Netherlands 2016 and Uganda 2012 , as follows. Mosquitoes were fed on seven birds: three of the wild-caught house sparrows used in the within-host models (N-Sp1, N-Sp9 and N-Sp3) and four juvenile chickens (bred for low antibody titres) τ = 2 days after they were inoculated with 1500 PFU Netherlands 2016 [11]. The paired bird viral load data (on log10 scale) at day 2, log10vj=log10Vjfalse(2false), and proportion of infected mosquitoes data bjd for bird inoculated with Netherlands 2016 is right left.5emthickmathspacetrueTn={1.2em1.2em(log10vj,bjd1.2em1.2em),j={1,2,…,7}}={(2.7,0.1);(5.98,0.16);(7.17,0.6);(3.04,0.125);(3.49,0.048);(3.66,0.1364);(2,0)},for N-Sp1, N-Sp9, N-Sp3 and four juvenile chickens, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…Note that the effect of viral loss through cell entry, given by −βTV, is negligible compared with virus production and clearance terms and, therefore, [11]. Empirical data.…”
Section: Within-host Scalementioning
confidence: 99%
“…Table 2. Monte Carlo simulation results for virtual datasets generated at each discrete experimental data point in [11] for (A) Netherlands 2016 and (B) Uganda 2012. AREs give the average relative estimation error for each parameter at noise level σ, and η determines whether each parameter is strongly (0 < η ≤ 1), weakly (1 < η ≤ 10) or non-identifiable (η > 10).…”
Understanding the epidemiology of emerging pathogens, such as Usutu virus (USUV) infections, requires systems investigation at each scale involved in the host–virus transmission cycle, from individual bird infections, to bird-to-vector transmissions, and to USUV incidence in bird and vector populations. For new pathogens field data are sparse, and predictions can be aided by the use of laboratory-type inoculation and transmission experiments combined with dynamical mathematical modelling. In this study, we investigated the dynamics of two strains of USUV by constructing mathematical models for the within-host scale, bird-to-vector transmission scale and vector-borne epidemiological scale. We used individual within-host infectious virus data and per cent mosquito infection data to predict USUV incidence in birds and mosquitoes. We addressed the dependence of predictions on model structure, data uncertainty and experimental design. We found that uncertainty in predictions at one scale change predicted results at another scale. We proposed
in silico
experiments that showed that sampling every 12 hours ensures practical identifiability of the within-host scale model. At the same time, we showed that practical identifiability of the transmission scale functions can only be improved under unrealistically high sampling regimes. Instead, we proposed optimal experimental designs and suggested the types of experiments that can ensure identifiability at the transmission scale and, hence, induce robustness in predictions at the epidemiological scale.
“…Understanding the epidemiology of emerging pathogens, such as USUV, requires systems investigation at each scale involved in the host-virus transmission cycle, from individual infection in birds, to bird-tovector transmission, to USUV incidence in bird and vector populations and eventually to spillover probability in humans [37][38][39]. For new pathogens, field data is sparse, and predictions are based on laboratory-type inoculation and transmission experiments [9,11] combined with dynamical mathematical modelling [9].…”
Section: Discussionmentioning
confidence: 99%
“…Note that the effect of viral loss through cell entry, given by − βTV , is negligible compared with virus production and clearance terms and, therefore, not included in the virus equation. We aim to estimate model equation (2.1)’s parameters by fitting it to published infectious virus titres in wild-caught house sparrows infected with either Netherlands 2016 or Uganda 2012 [11]. Figure 1Model diagram describing the within-host USUV infection.…”
Section: Methodsmentioning
confidence: 99%
“…Culex quinquefasciatus mosquitoes were fed on birds infected with USUV Netherlands 2016 and Uganda 2012 , as follows. Mosquitoes were fed on seven birds: three of the wild-caught house sparrows used in the within-host models (N-Sp1, N-Sp9 and N-Sp3) and four juvenile chickens (bred for low antibody titres) τ = 2 days after they were inoculated with 1500 PFU Netherlands 2016 [11]. The paired bird viral load data (on log10 scale) at day 2, log10vj=log10Vjfalse(2false), and proportion of infected mosquitoes data bjd for bird inoculated with Netherlands 2016 is right left.5emthickmathspacetrueTn={1.2em1.2em(log10vj,bjd1.2em1.2em),j={1,2,…,7}}={(2.7,0.1);(5.98,0.16);(7.17,0.6);(3.04,0.125);(3.49,0.048);(3.66,0.1364);(2,0)},for N-Sp1, N-Sp9, N-Sp3 and four juvenile chickens, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…Note that the effect of viral loss through cell entry, given by −βTV, is negligible compared with virus production and clearance terms and, therefore, [11]. Empirical data.…”
Section: Within-host Scalementioning
confidence: 99%
“…Table 2. Monte Carlo simulation results for virtual datasets generated at each discrete experimental data point in [11] for (A) Netherlands 2016 and (B) Uganda 2012. AREs give the average relative estimation error for each parameter at noise level σ, and η determines whether each parameter is strongly (0 < η ≤ 1), weakly (1 < η ≤ 10) or non-identifiable (η > 10).…”
Understanding the epidemiology of emerging pathogens, such as Usutu virus (USUV) infections, requires systems investigation at each scale involved in the host–virus transmission cycle, from individual bird infections, to bird-to-vector transmissions, and to USUV incidence in bird and vector populations. For new pathogens field data are sparse, and predictions can be aided by the use of laboratory-type inoculation and transmission experiments combined with dynamical mathematical modelling. In this study, we investigated the dynamics of two strains of USUV by constructing mathematical models for the within-host scale, bird-to-vector transmission scale and vector-borne epidemiological scale. We used individual within-host infectious virus data and per cent mosquito infection data to predict USUV incidence in birds and mosquitoes. We addressed the dependence of predictions on model structure, data uncertainty and experimental design. We found that uncertainty in predictions at one scale change predicted results at another scale. We proposed
in silico
experiments that showed that sampling every 12 hours ensures practical identifiability of the within-host scale model. At the same time, we showed that practical identifiability of the transmission scale functions can only be improved under unrealistically high sampling regimes. Instead, we proposed optimal experimental designs and suggested the types of experiments that can ensure identifiability at the transmission scale and, hence, induce robustness in predictions at the epidemiological scale.
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