Trips
EVENTS, HOOKWORM INFECTION
In our metapopulation model, the spatial spread of hookworm infection is facilitated by visits taken by individuals to locations in other pixels, We assume that the majority of travel an individual makes of duration less than a day, for example, school attendance or commuting for work, will be within their home pixel and is modelled by within cell transmission dynamics, therefore visits outside the home cell are assumed to last longer than a day, While on a visit, an individual is assumed to be exposed to infectious contact with larval stages in that environment, and are able to contribute to, and be infected by, the local reservoir of infective stages.For simplicity, we have each individual in the population equally likely to make visits (in reality, the propensity to take trips is strongly dependent on age, and other sociological factors which may, in some individuals, be correlated with worm burden). In each discrete timestep of the individual-based model, every individual in cell Each visit has a duration, that is, the length of time spent away from the resident pixel in a single visit, which is also generated randomly from a predefined probability distribution, where shorter duration trips are more likely than longer ones. Our model uses a fixed timestep of one month, therefore, the fractional part of the visit duration in months is used as a multiplicative factor of that individual's exposure in that month. We use a previously published empirical distribution from Namibia taken from call data records which recorded 259 million trips by 2.5 million mobile phone subscribers between 2010 and 2014 [Empirical cumulative distribution of trip lengths [The gravity model effectively generates a complete graph of interactions between subpopulations, in which there is a non-zero probability of individuals taking trips to all other pixels. To aid computational efficiency, we employ a minimum threshold set so that the expected number of trips between each pair of locations is no less than once per year, to filter out low probability trips. This supresses the possibility of low probability events, and may influence the tail of sampled distributions (in some circumstances changing the mean dynamics of transmission). However, we believe it will have limited bearing on overall results.