2.4. Conceptual framework of outbreak risk

A conceptual diagram was created to represent pathways by which hypothesized factors beyond vaccination could influence pathogen spread and the reported number of outbreaks per state (Figure S2), and accounting for these additional sources of variability may allow to better determine the effect of vaccination on outbreak counts at the state level. Details on risk factor variables included in the model are shown in Table S1. Disease spread is expected to be influenced by organized farming practices, and other community activities like animal fairs. Briefly, livestock population data of goat density and pig density were included in our model as categorical variables (high/low, split at the mean). In addition, higher outbreak numbers have been reported in dryer agroclimatic zones in some parts of India (Hegde et al., 2014), potentially due to husbandry practices that are typical of different climatic conditions or due to environmental conditions within those regions that promote survivability of the virus outside the host. Therefore, land cover and annual averages for wind speed, rainfall and temperature were included to capture environmental factors related to outbreak risk (Abatzoglou et al., 2018). Evaluation of intra‐annual and seasonal variation was not possible since outbreak numbers were reported as annual values. However, to quantify areas with more extreme seasonality, annual variance (calculated across 12 months) of each environmental variable was also included. All environmental variables were centred at the mean and standardized.

Animal transport within India or across international borders for trading and slaughter may promote disease spread and the occurrence of outbreaks. Because no digitized animal movement databases are maintained for India, road density was utilized as a proxy measure. India is bordered by Pakistan, China, Nepal, Bhutan, Myanmar and Bangladesh. FAO/OIE has categorized India and the surrounding endemic countries into three different ‘pools’ of FMD based on the predominant circulating serotypes and topotypes in each area (Paton et al., 2018). Pool 1 includes Myanmar and China; pool 2 includes India, Sri Lanka, Nepal, Bangladesh and Bhutan; and pool 3 includes Pakistan. Dummy variables were introduced to the analysis indicating whether each state was bordered by a pool 1, pool 2 or pool 3 country, or if the state did not have any international land borders (Table S1).

In addition, the presence and efficiency of veterinary services within a state may influence both vaccination as well as outbreak reporting. The coverage of veterinary services in each state was calculated based on the percentage of veterinarians available relative to the number of veterinarians estimated to be required by that state. This value was obtained from the OIE Performance of Veterinary Services analysis for India.

For each year, states were categorized into two groups based on whether they were part of the FMDCP. All states that had at least one district participating in the vaccination program at some point of time are shown in the Figure S3, and district‐level participation expanded through time. During the study period, the majority of districts within a state were enrolled in the program. Detailed information regarding the participation in vaccination program at the district level is available at https://dadfonline.gov.in/FMDCP/Index.aspx. There was no substantial correlation between any variables (Figures S4 and S5).