Comparing metapopulation dynamics of infectious diseases under different models of human movement.

Newly available datasets present exciting opportunities to investigate how human population movement contributes to the spread of infectious diseases across large geographical distances. It is now possible to construct realistic models of infectious disease dynamics for the purposes of understanding global-scale epidemics. Nevertheless, a remaining unanswered question is how best to leverage the new data to parameterize models of movement, and whether one's choice of movement model impacts modeled disease outcomes. We adapt three well-studied models of infectious disease dynamics, the susceptible-infected-recovered model, the susceptible-infected-susceptible model, and the Ross-Macdonald model, to incorporate either of two candidate movement models. We describe the effect that the choice of movement model has on each disease model's results, finding that in all cases, there are parameter regimes where choosing one movement model instead of another has a profound impact on epidemiological outcomes. We further demonstrate the importance of choosing an appropriate movement model using the applied case of malaria transmission and importation on Bioko Island, Equatorial Guinea, finding that one model produces intelligible predictions of R0, whereas the other produces nonsensical results.

Analysing human population movement data for malaria control and elimination.

BACKGROUND: Human population movement poses a major obstacle to malaria control and elimination. With recent technological advances, a wide variety of data sources and analytical methods have been used to quantify human population movement (HPM) relevant to control and elimination of malaria. METHODS: The relevant literature and selected studies that had policy implications that could help to design or target malaria control and elimination interventions were reviewed. These studies were categorized according to spatiotemporal scales of human mobility and the main method of analysis. RESULTS: Evidence gaps exist for tracking routine cross-border HPM and HPM at a regional scale. Few studies accounted for seasonality. Out of twenty included studies, two studies which tracked daily neighbourhood HPM used descriptive analyses as the main method, while the remaining studies used statistical analyses or mathematical modelling. CONCLUSION: Although studies quantified varying types of human population movement covering different spatial and temporal scales, methodological gaps remain that warrant further studies related to malaria control and elimination.

Human population movement and behavioural patterns in malaria hotspots on the Thai-Myanmar border: implications for malaria elimination.

BACKGROUND: Malaria is heterogeneously distributed across landscapes. Human population movement (HPM) could link sub-regions with varying levels of transmission, leading to the persistence of disease even in very low transmission settings. Malaria along the Thai-Myanmar border has been decreasing, but remains heterogeneous. This study aimed to measure HPM, associated predictors of travel, and HPM correlates of self-reported malaria among people living within malaria hotspots. METHODS: 526 individuals from 279 households in two malaria hotspot areas were included in a prospective observational study. A baseline cross-sectional study was conducted at the beginning, recording both individual- and household-level characteristics. Individual movement and travel patterns were repeatedly observed over one dry season month (March) and one wet season month (May). Descriptive statistics, random effects logistic regressions, and logistic regressions were used to describe and determine associations between HPM patterns, individual-, household-factors, and self-reported malaria. RESULTS: Trips were more common in the dry season. Malaria risk was related to the number of days doing outdoor activities in the dry season, especially trips to Myanmar, to forest areas, and overnight trips. Trips to visit forest areas were more common among participants aged 20-39, males, individuals with low income, low education, and especially among individuals with forest-related occupations. Overnight trips were more common among males, and individual with forest-related occupations. Forty-five participants reported having confirmed malaria infection within the last year. The main place of malaria blood examination and treatment was malaria post and malaria clinic, with participants usually waiting for 2-3 days from onset fever to seeking diagnosis. Individuals using bed nets, living in houses with elevated floors, and houses that received indoor residual spraying in the last year were less likely to report malaria infection. CONCLUSION: An understanding of HPM and concurrent malaria dynamics is important for consideration of targeted public health interventions. Furthermore, diagnosis and treatment centres must be capable of quickly diagnosing and treating infections regardless of HPM. Coverage of diagnosis and treatment centres should be broad, maintained in areas bordering malaria hotspots, and available to all febrile individuals.

Human population movement can impede the elimination of soil-transmitted helminth transmission in regions with heterogeneity in mass drug administration coverage and transmission potential between villages: a metapopulation analysis.

BACKGROUND: Soil-transmitted helminth (STH) infections affect predominantly socio-economically disadvantaged populations in sub-Saharan Africa, East Asia and the Americas. Previous mathematical modelling studies have evaluated optimal intervention strategies to break STH transmission in clusters of villages. These studies assumed that villages are closed independent units with no movement of people in or out of communities. Here we examine how human population movement, for example, of seasonal migrant labourers, affect the outcome of mass drug administration (MDA) programmes. RESULTS: We used a stochastic individual-based metapopulation model to analyse the impact of human population movement at varying rates on STH elimination efforts. Specifically, we looked at seasonal clumped movement events of infected individuals into a village. We showed that even if on average 75% of the entire resident population within a village are treated, an annual rate of 2-3% of the population arriving from an untreated source village can reduce the probability of STH elimination to less than 50% in high-prevalence settings. If a village is infection-free, an annual movement rate of 2-3% from an infected source village imposes a risk of re-introduction of STH of 75% or higher, unless the prevalence in the source village is less than 20%. Even a single arrival of 2-3% of the population can impose a risk of re-introducing STH of 50% or greater depending on the prevalence in the source village. The risk of re-introduction also depends on both the age group of moving individuals and STH species, since the pattern of cross-sectional age-prevalence and age-intensity profiles of infection in the human host are species-specific. CONCLUSIONS: Planning for STH elimination programmes should account for human mobility patterns in defined regions. We recommend that individuals arriving from areas with ongoing STH transmission should receive preventive chemotherapy for STHs. This can most easily be implemented if migration is seasonal and overlaps with treatment rounds, e.g. seasonal migrant labour. Moreover, transmission hotspots in or near treatment clusters should be eliminated, for example, by implementing appropriate water, sanitation and hygiene (WASH) measures and targeting treatment to individuals living in hotspots.