Human mobility patterns and malaria importation on Bioko Island

Abstract

Malaria burden on Bioko Island has decreased significantly over the past 15 years. The impact of interventions on malaria prevalence, however, has recently stalled. Here, we use data from island-wide, annual malaria indicator surveys to investigate human movement patterns and their relationship to Plasmodium falciparum prevalence. Using geostatistical and mathematical modelling, we find that off-island travel is more prevalent in and around the capital, Malabo. The odds of malaria infection among off-island travelers are significantly higher than the rest of the population. We estimate that malaria importation rates are high enough to explain malaria prevalence in much of Malabo and its surroundings, and that local transmission is highest along the West Coast of the island. Despite uncertainty, these estimates of residual transmission and importation serve as a basis for evaluating progress towards elimination and for efficiently allocating resources as Bioko makes the transition from control to elimination.

Comparing Metapopulation Dynamics of Infectious Diseases under Different Models of Human Movement

“…In this context, R0 < 0 has no meaningful real-world interpretation beyond indicating that the data are inconsistent with the chosen mechanistic model. (14). Each pixel shows the mean estimated prevalence within a 1 × 1 km 2 area.…”

“…Census data attempts to describe population migration that occurs between census years (8). Traditional surveys of commuters (9,10), patients (11,12), or residents in disease-affected areas (13,14) provide another description of recent travel activity. More recently, mobile phone service providers have shared privacy-protected data sets, showing how large numbers of users tend to move between cell phone towers (12,(15)(16)(17)(18).…”

Measuring the accuracy of gridded human population density surfaces: a case study in Bioko Island, Equatorial Guinea

“…In Table 1, WP has the lowest population totals and lowest max population density, this is likely due to WP maxing out population at 1000 people per km 2 , which is a low estimation for a primarily urban population like we see in Bioko Island where a majority of the population lives in Malabo. The average population density of Malabo is around 14,000 per km 2 (9) which results in a significant under count, and this effect is increased when the resolution is increased to 100 m. On the opposite end of the population spectrum, WP severely overestimates population densities everywhere else, with the lowest number of empty pixels (Fig. 5).…”

“…Pf PR mapping We estimated the prevalence of malaria parasites, a Pf PR surface, for Bioko Island using the same set of covariates, replacing only the population surfaces one at a time. Data and methods described elsewhere (8,9). The population surfaces were then used to construct density values to assign as population weights for use in the calculation of Pf PR.…”

“…Risk factors, health catchment populations, access to resources, and operational constraints on provisioning resources are intrinsically related to population density and geographical location. In the context of malaria, population surfaces are also increasingly used as covariates for geostatistical models for mapping prevalence, incidence and other metrics (6)(7)(8)(9). Information about the geographical location of human households is required to weigh resource allocation decisions to identify and serve populations in need (10,11).…”