Abstract
African Swine Fever (ASF) was first reported in Laos in June 2019, when an incursion caused 150 village outbreaks across all Provinces and territories. Despite stamping out control measures, ASF virus (ASFV) spread rapidly. Understanding the rate of disease spread and the infectious period of ASFV at the village level is necessary to accurately model this disease in the smallholder system which exists in countries such as Laos. However, such parameter estimates are scarce. The aim of this study was to estimate key epidemiologic parameters to enable modelling of ASFV spread in Laos.
Data were collected on ASF outbreaks reported and confirmed via PCR testing during the period 1 June 2019 to 1 January 2020. Information included outbreak date or date of first report, and village location. The latter was used to determine latitude and longitude coordinates.
To estimate disease spread parameters, the Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) technique was used to validate simulated disease parameters against the reported ASF outbreaks using a compartmental disease model. A prior for between-village transmission was based on ASFV outbreaks between smallholder farms in Uganda, represented as a gamma distribution. For the latent (mean, shape) and mean infectious periods, various estimates based on experimental and field studies from published studies were used.
A critical assumption is homogenous mixing within the modeled population. To better meet this assumption, the outbreak data were analysed to identify likely spatiotemporal clusters; these clusters were assumed to represent the occurrence of linked, village spread of ASFV.
The final dataset contained 150 outbreak villages representing all Provinces in Laos. Six spatiotemporal clusters were identified. The size of these clusters ranged from 4 to 14 case villages, radii 16 to 153km and period 0 to 22 days.
The mean estimated latent period was 5.82 to 5.95 days across the clusters, the mean village infectious period 61.53 to 67.70 days, and mean β 0.19 to 0.51. The R0 ranged from 13.06 to 31.06 across the clusters. Simulated outbreaks utilising the estimated transmission parameters struggled to match the observed data in clusters in which fewer than ten outbreaks occurred or where all outbreaks were reported over only one to two days.
Spatio-temporal analysis can be used to improve ASFV transmission parameter estimation, thus improving outputs from disease spread models. However, this study demonstrates that the value provided by such approaches can be limited by the surveillance data that is available.