Quantifying the impact of human mobility on malaria.
Ontology highlight
ABSTRACT:
SUBMITTER: Wesolowski A
PROVIDER: S-EPMC3675794 | biostudies-literature | 2012 Oct
REPOSITORIES: biostudies-literature
Publications
Quantifying the impact of human mobility on malaria.
Wesolowski Amy A Eagle Nathan N Tatem Andrew J AJ Smith David L DL Noor Abdisalan M AM Snow Robert W RW Buckee Caroline O CO
Science (New York, N.Y.) 20121001 6104
Human movements contribute to the transmission of malaria on spatial scales that exceed the limits of mosquito dispersal. Identifying the sources and sinks of imported infections due to human travel and locating high-risk sites of parasite importation could greatly improve malaria control programs. Here, we use spatially explicit mobile phone data and malaria prevalence information from Kenya to identify the dynamics of human carriers that drive parasite importation between regions. Our analysis ...[more]
Similar Datasets
Project description:Malaria antigen detection through rapid diagnostic tests (RDTs) is widely used to diagnose malaria and estimate prevalence. To support more sensitive next-generation RDT development and screen asymptomatic malaria, we developed and evaluated the Q-Plex™ Human Malaria Array (Quansys Biosciences, Logan, UT), which quantifies the antigens commonly used in RDTs-Plasmodium falciparum-specific histidine-rich protein 2 (HRP2), P. falciparum-specific lactate dehydrogenase (Pf LDH), Plasmodium vivax -specific LDH (Pv LDH), and Pan malaria lactate dehydrogenase (Pan LDH), and human C-reactive protein (CRP), a biomarker of severity in malaria. At threshold levels yielding 99.5% or more diagnostic specificity, diagnostic sensitivities against polymerase chain reaction-confirmed malaria for HRP2, Pf LDH, Pv LDH, and Pan LDH were 92.7%, 71.5%, 46.1%, and 83.8%, respectively. P. falciparum culture strains and samples from Peru indicated that HRP2 and Pf LDH combined improves detection of P. falciparum parasites with hrp2 and hrp3 deletions. This array can be used for antigen-based malaria screening and detecting hrp2/3 deletion mutants of P. falciparum.
| S-EPMC7253106 | biostudies-literature
Project description:Quantifying global international mobility patterns can improve migration governance. Despite decades of calls by the international community to improve international migration statistics, the availability of timely and disaggregated data about long-term and short-term migration at the global level is still very limited. In this study, we investigate the feasibility of using non-traditional data sources to fill existing gaps in migration statistics. To this end, we use anonymised and publicly available data provided by Facebook's advertising platform. Facebook's advertising platform classifies its users as "lived in country X" if they previously lived in country X, and now live in a different country. Drawing on statistics about Facebook Network users (Facebook, Instagram, Messenger, and the Audience Network) who have lived abroad and applying a sample bias correction method, we estimate the number of Facebook Network (FN) "migrants" in 119 countries of residence and in two time periods by age, gender, and country of previous residence. The correction method estimates the probability of a person being a FN user based on age, sex, and country of current and previous residence. We further estimate the correlation between FN-derived migration estimates and reference official migration statistics. By comparing FN-derived migration estimates in two different time periods, January-February and August-September 2018, we successfully capture the increase in Venezuelan migrants in Colombia and Spain in 2018. FN-derived migration estimates cannot replace official migration statistics, as they are not representative, and the exact methods the FN uses for classifying its users are not known, and might change over time. However, after carefully assessing the validity of the FN-derived estimates by comparing them with data from reliable sources, we conclude that these estimates can be used for trend analysis and early-warning purposes.
| S-EPMC6812739 | biostudies-literature
Project description:Human mobility is influenced by environmental change and natural disasters. Researchers have used trip distance distribution, radius of gyration of movements, and individuals' visited locations to understand and capture human mobility patterns and trajectories. However, our knowledge of human movements during natural disasters is limited owing to both a lack of empirical data and the low precision of available data. Here, we studied human mobility using high-resolution movement data from individuals in New York City during and for several days after Hurricane Sandy in 2012. We found the human movements followed truncated power-law distributions during and after Hurricane Sandy, although the β value was noticeably larger during the first 24 hours after the storm struck. Also, we examined two parameters: the center of mass and the radius of gyration of each individual's movements. We found that their values during perturbation states and steady states are highly correlated, suggesting human mobility data obtained in steady states can possibly predict the perturbation state. Our results demonstrate that human movement trajectories experienced significant perturbations during hurricanes, but also exhibited high resilience. We expect the study will stimulate future research on the perturbation and inherent resilience of human mobility under the influence of hurricanes. For example, mobility patterns in coastal urban areas could be examined as hurricanes approach, gain or dissipate in strength, and as the path of the storm changes. Understanding nuances of human mobility under the influence of such disasters will enable more effective evacuation, emergency response planning and development of strategies and policies to reduce fatality, injury, and economic loss.
| S-EPMC4237337 | biostudies-literature
Project description: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.
| S-EPMC6536527 | biostudies-literature
Project description:BackgroundTo achieve malaria elimination, it is important to determine the role of human mobility in parasite transmission maintenance. The Alto Juruá basin (Brazil) exhibits one of the largest vivax and falciparum malaria prevalence in the Amazon. The goal of this study was to estimate the contribution of human commutes to malaria persistence in this region, using data from an origin-destination survey.MethodsData from an origin-destination survey were used to describe the intensity and motivation for commutations between rural and urban areas in two Alto Juruá basin (Brazil) municipalities, Mâncio Lima and Rodrigues Alves. The relative time-person spent in each locality per household was estimated. A logistic model was developed to estimate the effect of commuting on the probability of contracting malaria for a certain residence zone inhabitant commuting to another zone.ResultsThe main results suggest that the assessed population is not very mobile. A total of [Formula: see text] households reported spending over [Formula: see text] of their annual person-hour in areas within the same residence zone. Study and work were the most prevalent commuting motivations, calculated at [Formula: see text] and [Formula: see text] respectively. Spending person-hours in urban Rodrigues Alves conferred relative protection to urban Mâncio Lima residents. The opposite effect was observed for those spending time in rural areas of both municipalities.ConclusionResidence area is a stronger determinant for contracting malaria than commuting zones in the Alto Juruá region. As these municipalities are a hotspot for Plasmodium transmission, understanding the main local human fluxes is essential for planning control strategies, since the probability of contracting malaria is dependent on the transmission intensity of both the origin and the displacement area. The natural conditions for the circulation of certain pathogens, such as Plasmodium spp., combined with the Amazon human mobility pattern indicate the need for disease control perspective changes. Therefore, intersectoral public policies should become the basis for health mitigation actions.
| S-EPMC7659106 | biostudies-literature
Project description:Humans move frequently and tend to carry parasites among areas with endemic malaria and into areas where local transmission is unsustainable. Human-mediated parasite mobility can thus sustain parasite populations in areas where they would otherwise be absent. Data describing human mobility and malaria epidemiology can help classify landscapes into parasite demographic sources and sinks, ecological concepts that have parallels in malaria control discussions of transmission foci. By linking transmission to parasite flow, it is possible to stratify landscapes for malaria control and elimination, as sources are disproportionately important to the regional persistence of malaria parasites. Here, we identify putative malaria sources and sinks for pre-elimination Namibia using malaria parasite rate (PR) maps and call data records from mobile phones, using a steady-state analysis of a malaria transmission model to infer where infections most likely occurred. We also examined how the landscape of transmission and burden changed from the pre-elimination setting by comparing the location and extent of predicted pre-elimination transmission foci with modeled incidence for 2009. This comparison suggests that while transmission was spatially focal pre-elimination, the spatial distribution of cases changed as burden declined. The changing spatial distribution of burden could be due to importation, with cases focused around importation hotspots, or due to heterogeneous application of elimination effort. While this framework is an important step towards understanding progressive changes in malaria distribution and the role of subnational transmission dynamics in a policy-relevant way, future work should account for international parasite movement, utilize real time surveillance data, and relax the steady state assumption required by the presented model.
| S-EPMC4820264 | biostudies-literature
Project description:For countries aiming for malaria elimination, travel of infected individuals between endemic areas undermines local interventions. Quantifying parasite importation has therefore become a priority for national control programs. We analyzed epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data to measure the spatial spread of malaria parasites in southeast Bangladesh. We developed a genetic mixing index to estimate the likelihood of samples being local or imported from parasite genetic data and inferred the direction and intensity of parasite flow between locations using an epidemiological model integrating the travel survey and mobile phone calling data. Our approach indicates that, contrary to dogma, frequent mixing occurs in low transmission regions in the southwest, and elimination will require interventions in addition to reducing imported infections from forested regions. Unlike risk maps generated from clinical case counts alone, therefore, our approach distinguishes areas of frequent importation as well as high transmission.
| S-EPMC6478433 | biostudies-literature
Project description:As Africa-wide malaria prevalence declines, an understanding of human movement patterns is essential to inform how best to target interventions. We fitted movement models to trip data from surveys conducted at 3-5 sites throughout each of Mali, Burkina Faso, Zambia and Tanzania. Two models were compared in terms of their ability to predict the observed movement patterns - a gravity model, in which movement rates between pairs of locations increase with population size and decrease with distance, and a radiation model, in which travelers are cumulatively "absorbed" as they move outwards from their origin of travel. The gravity model provided a better fit to the data overall and for travel to large populations, while the radiation model provided a better fit for nearby populations. One strength of the data set was that trips could be categorized according to traveler group - namely, women traveling with children in all survey countries and youth workers in Mali. For gravity models fitted to data specific to these groups, youth workers were found to have a higher travel frequency to large population centers, and women traveling with children a lower frequency. These models may help predict the spatial transmission of malaria parasites and inform strategies to control their spread.
| S-EPMC5955928 | biostudies-literature
Project description:Many microparasites infect new hosts with specialized life stages, requiring a subset of the parasite population to forgo proliferation and develop into transmission forms. Transmission stage production influences infectivity, host exploitation, and the impact of medical interventions like drug treatment. Predicting how parasites will respond to public health efforts on both epidemiological and evolutionary timescales requires understanding transmission strategies. These strategies can rarely be observed directly and must typically be inferred from infection dynamics. Using malaria as a case study, we test previously described methods for inferring transmission stage investment against simulated data generated with a model of within-host infection dynamics, where the true transmission investment is known. We show that existing methods are inadequate and potentially very misleading. The key difficulty lies in separating transmission stages produced by different generations of parasites. We develop a new approach that performs much better on simulated data. Applying this approach to real data from mice infected with a single Plasmodium chabaudi strain, we estimate that transmission investment varies from zero to 20%, with evidence for variable investment over time in some hosts, but not others. These patterns suggest that, even in experimental infections where host genetics and other environmental factors are controlled, parasites may exhibit remarkably different patterns of transmission investment.
| S-EPMC4759450 | biostudies-literature
Project description:The COVID-19 pandemic has brought huge challenges to sustainable urban and community development. Although some recovery signals and patterns have been uncovered, the intra-city recovery process remains underexploited. This study proposes a comprehensive approach to quantify COVID-19 recovery leveraging fine-grained human mobility records. Taking Wuhan, a typical COVID-19 affected megacity in China, as the study area, we identify accurate recovery phases and select appropriate recovery functions in a data-driven manner. We observe that recovery characteristics regarding duration, amplitude, and velocity exhibit notable differences among urban blocks. We also notice that the recovery process under a one-wave outbreak lasts at least 84 days and has an S-shaped form best fitted with four-parameter Logistic functions. More than half of the recovery variance can be well explained and estimated by common variables from auxiliary data, including population, economic level, and built environments. Our study serves as a valuable reference that supports data-driven recovery quantification for COVID-19 and other crises.
| S-EPMC9659556 | biostudies-literature