A Mathematical Model that Simulates Control Options for African Swine Fever Virus (ASFV)

Abstract

A stochastic model designed to simulate transmission dynamics of African swine fever virus (ASFV) in a free-ranging pig population under various intervention scenarios is presented. The model was used to assess the relative impact of the timing of the implementation of different control strategies on disease-related mortality. The implementation of biosecurity measures was simulated through incorporation of a decay function on the transmission rate. The model predicts that biosecurity measures implemented within 14 days of the onset of an epidemic can avert up to 74% of pig deaths due to ASF while hypothetical vaccines that confer 70% immunity when deployed prior to day 14 of the epidemic could avert 65% of pig deaths. When the two control measures are combined, the model predicts that 91% of the pigs that would have otherwise succumbed to the disease if no intervention was implemented would be saved. However, if the combined interventions are delayed (defined as implementation from > 60 days) only 30% of ASF-related deaths would be averted. In the absence of vaccines against ASF, we recommend early implementation of enhanced biosecurity measures. Active surveillance and use of pen-side diagnostic assays, preferably linked to rapid dissemination of this data to veterinary authorities through mobile phone technology platforms are essential for rapid detection and confirmation of ASF outbreaks. This prediction, although it may seem intuitive, rationally confirms the importance of early intervention in managing ASF epidemics. The modelling approach is particularly valuable in that it determines an optimal timing for implementation of interventions in controlling ASF outbreaks.

Fig 1. The schema shows the transition pathways between epidemiological classes of the ASF model.

The transition from class (S) to class (E) was governed by transmission rate (β) while the transition from class (E) to class (I) was dependent on latent period (σ). The infectious animals either die at a rate (γρ) and enter class (D) or enter the carrier class (C) at a rate γ(1−ρ). Carriers also transmit at a reduced rate (εβ) and can re-activate to infectiousness at a rate (κ). There is natural mortality that occurs in each class at a rate μ. New recruits enter the S class at a rate μN.

Fig 2. Box plots showing the effect of timing of introduction of different intervention scenarios on disease burden.

The baseline box represents an intervention-free scenario. Panel (a) shows the effect of the timing of introduction of biosecurity measures after the onset of the epidemic (where Bio_xx = Biosecurity strategy implemented at day xx). Panel (b) depicts effects of vaccination (protecting 50%) implemented at day 14, 30 and 60 days on disease burden (i.e. Vac_50xx = Vaccination conferring 50% protection at day xx). Panel (c) compares the effects of different vaccine efficacies and a combination intervention strategy on disease burden when intervention is started at day 14 (Bio_Vac_7014 = Combination of biosecurity and 70% Vaccine efficacy implemented at day 14). Panel (d) depicts the effects of delayed intervention on disease burden across different strategies of vaccine efficacies and combination scenarios (i.e. Vac_yyxx = Pulse Vaccination of efficacy yy% implemented at day xx while Bio_Vac_7060 is a combination strategy of Biosecurity measures and 70% efficacy vaccine implemented at day 60).

Fig 3. Box plots comparing different intervention scenarios at day 14 and day 60.

Panel (a) shows relative impact of the intervention scenarios at day 14. Bio_Vac_7014 is a combination of biosecurity measures and vaccination with 70% effect at day 14. Box Ctns_Vac_7014 is a scenario of pulse vaccination at day 14 followed by continuous vaccination programme of new recruits. Panel (b) compares the effects similar intervention scenarios at day 14 and day 60 to show the effect of timing of intervention on disease burden irrespective of the strategy implemented.