Deepa Pindolia

Human Movement Data for Malaria Control and Elimination Strategic Planning

Recent increases in funding for malaria control have led to the reduction in transmission in many malaria endemic countries, prompting the national control programmes of 36 malaria endemic countries to set elimination targets. Accounting for human population movement (HPM) in planning for control, elimination and post-elimination surveillance is important, as evidenced by previous elimination attempts that were undermined by the reintroduction of malaria through HPM. Strategic control and elimination planning, therefore, requires quantitative information on HPM patterns and the translation of these into parasite dispersion. HPM patterns and the risk of malaria vary substantially across spatial and temporal scales, demographic and socioeconomic sub-groups, and motivation for travel, so multiple data sets are likely required for quantification of movement. While existing studies based on mobile phone call record data combined with malaria transmission maps have begun to address within-country HPM patterns, other aspects remain poorly quantified despite their importance in accurately gauging malaria movement patterns and building control and detection strategies, such as cross-border HPM, demographic and socioeconomic stratification of HPM patterns, forms of transport, personal malaria protection and other factors that modify malaria risk. A wealth of data exist to aid filling these gaps, which, when combined with spatial data on transport infrastructure, traffic and malaria transmission, can answer relevant questions to guide strategic planning. This review aims to (i) discuss relevant types of HPM across spatial and temporal scales, (ii) document where datasets exist to quantify HPM, (iii) highlight where data gaps remain and (iv) briefly put forward methods for integrating these datasets in a Geographic Information System (GIS) framework for analysing and modelling human population and Plasmodium falciparum malaria infection movements.

Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning

BackgroundAs successful malaria control programmes re-orientate towards elimination, the identification of transmission foci, targeting of attack measures to high-risk areas and management of importation risk become high priorities. When resources are limited and transmission is varying seasonally, approaches that can rapidly prioritize areas for surveillance and control can be valuable, and the most appropriate attack measure for a particular location is likely to differ depending on whether it exports or imports malaria infections.Methods/ResultsHere, using the example of Namibia, a method for targeting of interventions using surveillance data, satellite imagery, and mobile phone call records to support elimination planning is described. One year of aggregated movement patterns for over a million people across Namibia are analyzed, and linked with case-based risk maps built on satellite imagery. By combining case-data and movement, the way human population movements connect transmission risk areas is demonstrated. Communities that were strongly connected by relatively higher levels of movement were then identified, and net export and import of travellers and infection risks by region were quantified. These maps can aid the design of targeted interventions to maximally reduce the number of cases exported to other regions while employing appropriate interventions to manage risk in places that import them.ConclusionsThe approaches presented can be rapidly updated and used to identify where active surveillance for both local and imported cases should be increased, which regions would benefit from coordinating efforts, and how spatially progressive elimination plans can be designed. With improvements in surveillance systems linked to improved diagnosis of malaria, detailed satellite imagery being readily available and mobile phone usage data continually being collected by network providers, the potential exists to make operational use of such valuable, complimentary and contemporary datasets on an ongoing basis in infectious disease control and elimination.

Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation.

The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models.Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites.In this paper we discuss the deficiencies of existing spatial population datasets and their limitations on epidemiological analyses. We review sources of detailed, contemporary, freely available and relevant spatial demographic data focusing on low income regions where such data are often sparse and highlight the value of incorporating these through a set of examples of their application in disease studies. Moreover, the importance of acknowledging, measuring, and accounting for uncertainty in spatial demographic datasets is outlined. Finally, a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications is put forward.

The use of census migration data to approximate human movement patterns across temporal scales

Human movement plays a key role in economies and development, the delivery of services, and the spread of infectious diseases. However, it remains poorly quantified partly because reliable data are often lacking, particularly for low-income countries. The most widely available are migration data from human population censuses, which provide valuable information on relatively long timescale relocations across countries, but do not capture the shorter-scale patterns, trips less than a year, that make up the bulk of human movement. Census-derived migration data may provide valuable proxies for shorter-term movements however, as substantial migration between regions can be indicative of well connected places exhibiting high levels of movement at finer time scales, but this has never been examined in detail. Here, an extensive mobile phone usage data set for Kenya was processed to extract movements between counties in 2009 on weekly, monthly, and annual time scales and compared to data on change in residence from the national census conducted during the same time period. We find that the relative ordering across Kenyan counties for incoming, outgoing and between-county movements shows strong correlations. Moreover, the distributions of trip durations from both sources of data are similar, and a spatial interaction model fit to the data reveals the relationships of different parameters over a range of movement time scales. Significant relationships between census migration data and fine temporal scale movement patterns exist, and results suggest that census data can be used to approximate certain features of movement patterns across multiple temporal scales, extending the utility of census-derived migration data.

Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination

BackgroundIdentifying human and malaria parasite movements is important for control planning across all transmission intensities. Imported infections can reintroduce infections into areas previously free of infection, maintain ‘hotspots’ of transmission and import drug resistant strains, challenging national control programmes at a variety of temporal and spatial scales. Recent analyses based on mobile phone usage data have provided valuable insights into population and likely parasite movements within countries, but these data are restricted to sub-national analyses, leaving important cross-border movements neglected.MethodsNational census data were used to analyse and model cross-border migration and movement, using East Africa as an example. ‘Hotspots’ of origin-specific immigrants from neighbouring countries were identified for Kenya, Tanzania and Uganda. Populations of origin-specific migrants were compared to distance from origin country borders and population size at destination, and regression models were developed to quantify and compare differences in migration patterns. Migration data were then combined with existing spatially-referenced malaria data to compare the relative propensity for cross-border malaria movement in the region.ResultsThe spatial patterns and processes for immigration were different between each origin and destination country pair. Hotspots of immigration, for example, were concentrated close to origin country borders for most immigrants to Tanzania, but for Kenya, a similar pattern was only seen for Tanzanian and Ugandan immigrants. Regression model fits also differed between specific migrant groups, with some migration patterns more dependent on population size at destination and distance travelled than others. With these differences between immigration patterns and processes, and heterogeneous transmission risk in East Africa and the surrounding region, propensities to import malaria infections also likely show substantial variations.ConclusionThis was a first attempt to quantify and model cross-border movements relevant to malaria transmission and control. With national census available worldwide, this approach can be translated to construct a cross-border human and malaria movement evidence base for other malaria endemic countries. The outcomes of this study will feed into wider efforts to quantify and model human and malaria movements in endemic regions to facilitate improved intervention planning, resource allocation and collaborative policy decisions.

Estimated impact and cost-effectiveness of rotavirus vaccination in India: Effects of geographic and economic disparities

India accounts for 23% of global rotavirus mortality in under-five children, with more than 100,000 deaths from rotavirus annually. Introduction of a vaccine in India is considered to be the most effective intervention for preventing rotavirus mortality. Recent research suggests that there is considerable variation in rotavirus mortality burden across regional, gender and socio-economic subpopulations within India. In addition, there is potential variability in who would likely receive rotavirus vaccine if introduced. We use available household data to estimate heterogeneity in rotavirus mortality risk, vaccination benefits, and cost-effectiveness across geographic and socio-economic groups within India. We account for heterogeneity by modeling estimated three-dose routine vaccinations as a proxy for a generalized rotavirus vaccine, and mortality for subpopulations of children aggregated by region and state, socio-economic status and sex, separately. Results are presented for six geographic regions and for Bihar, Uttar Pradesh, and Madhya Pradesh, three high mortality states accounting for 56% of national mortality estimates. Impact estimates accounting for disparities predict rotavirus vaccine introduction will prevent 35,000 deaths at an average cost of

Advances in mapping malaria for elimination: fine resolution modelling of Plasmodium falciparum incidence

118/DALY averted (7292 INR/DALY averted). Rotavirus vaccines are most cost-effective for the poor living in high mortality regions and states. Reductions in geographic and socio-economic disparities based on regional estimates could prevent an additional 9400 deaths annually, while reductions in socio-economic disparities in the three highest morality states alone could prevent an additional 10,600 deaths annually. Understanding the impact of heterogeneity can help improve strategies to maximize the benefits of rotavirus vaccination introduction, leading to fewer lives lost as a result of rotavirus disease.

Travel patterns and demographic characteristics of malaria cases in Swaziland, 2010-2014.

The long-term goal of the global effort to tackle malaria is national and regional elimination and eventually eradication. Fine scale multi-temporal mapping in low malaria transmission settings remains a challenge and the World Health Organisation propose use of surveillance in elimination settings. Here, we show how malaria incidence can be modelled at a fine spatial and temporal resolution from health facility data to help focus surveillance and control to population not attending health facilities. Using Namibia as a case study, we predicted the incidence of malaria, via a Bayesian spatio-temporal model, at a fine spatial resolution from parasitologically confirmed malaria cases and incorporated metrics on healthcare use as well as measures of uncertainty associated with incidence predictions. We then combined the incidence estimates with population maps to estimate clinical burdens and show the benefits of such mapping to identifying areas and seasons that can be targeted for improved surveillance and interventions. Fine spatial resolution maps produced using this approach were then used to target resources to specific local populations, and to specific months of the season. This remote targeting can be especially effective where the population distribution is sparse and further surveillance can be limited to specific local areas.

Low-quality housing is associated with increased risk of malaria infection: A national population-based study from the low transmission setting of Swaziland

BackgroundAs Swaziland progresses towards national malaria elimination, the importation of parasites into receptive areas becomes increasingly important. Imported infections have the potential to instigate local transmission and sustain local parasite reservoirs.MethodsTravel histories from Swaziland’s routine surveillance data from January 2010 to June 2014 were extracted and analysed. The travel patterns and demographics of rapid diagnostic test (RDT)-confirmed positive cases identified through passive and reactive case detection (RACD) were analysed and compared to those found to be negative through RACD.ResultsOf 1517 confirmed cases identified through passive surveillance, 67% reported travel history. A large proportion of positive cases reported domestic or international travel history (65%) compared to negative cases (10%). The primary risk factor for malaria infection in Swaziland was shown to be travel, more specifically international travel to Mozambique by 25- to 44-year old males, who spent on average 28 nights away. Maputo City, Inhambane and Gaza districts were the most likely travel destinations in Mozambique, and 96% of RDT-positive international travellers were either Swazi (52%) or Mozambican (44%) nationals, with Swazis being more likely to test negative. All international travellers were unlikely to have a bed net at home or use protection of any type while travelling. Additionally, paths of transmission, important border crossings and means of transport were identified.ConclusionResults from this analysis can be used to direct national and well as cross-border targeting of interventions, over space, time and by sub-population. The results also highlight that collaboration between neighbouring countries is needed to tackle the importation of malaria at the regional level.

The demographics of human and malaria movement and migration patterns in East Africa

Abstract Background Low-quality housing may confer risk of malaria infection, but evidence in low transmission settings is limited. Methods To examine the relationship between individual level housing quality and locally acquired infection in children and adults, a population-based cross-sectional analysis was performed using existing surveillance data from the low transmission setting of Swaziland. From 2012 to 2015, cases were identified through standard diagnostics in health facilities and by loop-mediated isothermal amplification in active surveillance, with uninfected subjects being household members and neighbors. Housing was visually assessed in a home visit and then classified as low, high, or medium quality, based on housing components being traditional, modern, or both, respectively. Results Overall, 11426 individuals were included in the study: 10960 uninfected and 466 infected (301 symptomatic and 165 asymptomatic). Six percent resided in low-quality houses, 26% in medium-quality houses, and 68% in high-quality houses. In adjusted models, low- and medium-quality construction was associated with increased risk of malaria compared with high-quality construction (adjusted odds ratio [AOR], 2.11 and 95% confidence interval [CI], 1.26–3.53 for low vs high; AOR, 1.56 and 95% CI, 1.15–2.11 for medium vs high). The relationship was independent of vector control, which also conferred a protective effect (AOR, 0.67; 95% CI, .50–.90) for sleeping under an insecticide-treated bed net or a sprayed structure compared with neither. Conclusions Our study adds to the limited literature on housing quality and malaria risk from low transmission settings. Housing improvements may offer an attractive and sustainable additional strategy to support countries in malaria elimination.