Estimating sources and sinks of malaria parasites in Madagascar

Spatiotemporal analysis of within-country imported malaria in Brazilian municipalities, 2004-2022

Human mobility has challenged malaria elimination efforts and remains difficult to routinely track. In Brazil, administrative records from the Ministry of Health allow monitoring of mobility locally and internationally. Although most imported malaria cases are between municipalities in Brazil, detailed knowledge of patterns of mobility is limited. Here, we address this gap by quantifying and describing patterns of malaria-infected individuals across the Amazon. We used network analysis, spatial clustering, and linear models to quantify and characterize the movement of malaria cases in Brazil between 2004 and 2022. We identified sources and sinks of malaria within and between states. We found that between-state movement of cases has become proportionally more important than within-state, that source clusters persisted longer than sink clusters, that movement of cases into sinks was seasonal while movement out of sources was not, and that importation is an impediment for subnational elimination in many municipalities. We elucidate the vast travel networks of malaria infected individuals that characterize the Amazon region. Uncovering patterns of malaria case mobility is vital for effective microstratification within Brazil. Our results have implications for intervention stratification across Brazil in line with the country's goal of malaria elimination by 2035.

Spatial spread of COVID-19 during the early pandemic phase in Italy

Quantifying the potential spatial spread of an infectious pathogen is key to defining effective containment and control strategies. The aim of this study is to estimate the risk of SARS-CoV-2 transmission at different distances in Italy before the first regional lockdown was imposed, identifying important sources of national spreading. To do this, we leverage on a probabilistic model applied to daily symptomatic cases retrospectively ascertained in each Italian municipality with symptom onset between January 28 and March 7, 2020. Results are validated using a multi-patch dynamic transmission model reproducing the spatiotemporal distribution of identified cases. Our results show that the contribution of short-distance ( ≤ 10 k m ) transmission increased from less than 40% in the last week of January to more than 80% in the first week of March 2020. On March 7, 2020, that is the day before the first regional lockdown was imposed, more than 200 local transmission foci were contributing to the spread of SARS-CoV-2 in Italy. At the time, isolation measures imposed only on municipalities with at least ten ascertained cases would have left uncontrolled more than 75% of spillover transmission from the already affected municipalities. In early March, national-wide restrictions were required to curb short-distance transmission of SARS-CoV-2 in Italy.

Genomics reveals heterogeneous Plasmodium falciparum transmission and selection signals in Zambia

BACKGROUND

Genomic surveillance is crucial for monitoring malaria transmission and understanding parasite adaptation to interventions. Zambia lacks prior nationwide efforts in malaria genomic surveillance among African countries.

METHODS

We conducted genomic surveillance of Plasmodium falciparum parasites from the 2018 Malaria Indicator Survey in Zambia, a nationally representative household survey of children under five years of age. We whole-genome sequenced and analyzed 241 P. falciparum genomes from regions with varying levels of malaria transmission across Zambia and estimated genetic metrics that are informative about transmission intensity, genetic relatedness between parasites, and selection.

RESULTS

We provide genomic evidence of widespread within-host polygenomic infections, regardless of epidemiological characteristics, underscoring the extensive and ongoing endemic malaria transmission in Zambia. Our analysis reveals country-level clustering of parasites from Zambia and neighboring regions, with distinct separation in West Africa. Within Zambia, identity by descent (IBD) relatedness analysis uncovers local spatial clustering and rare cases of long-distance sharing of closely related parasite pairs. Genomic regions with large shared IBD segments and strong positive selection signatures implicate genes involved in sulfadoxine-pyrimethamine and artemisinin combination therapies drug resistance, but no signature related to chloroquine resistance. Furthermore, differences in selection signatures, including drug resistance loci, are observed between eastern and western Zambian parasite populations, suggesting variable transmission intensity and ongoing drug pressure.

CONCLUSIONS

Our findings enhance our understanding of nationwide P. falciparum transmission in Zambia, establishing a baseline for analyzing parasite genetic metrics as they vary over time and space. These insights highlight the urgency of strengthening malaria control programs and surveillance of antimalarial drug resistance.

Genomics reveals heterogeneous Plasmodium falciparum transmission and population differentiation in Zambia and bordering countries

Genomic surveillance plays a critical role in monitoring malaria transmission and understanding how the parasite adapts in response to interventions. We conducted genomic surveillance of malaria by sequencing 241 Plasmodium falciparum genomes from regions with varying levels of malaria transmission across Zambia. We found genomic evidence of high levels of within-host polygenomic infections, regardless of epidemiological characteristics, underscoring the extensive and ongoing endemic malaria transmission in the country. We identified country-level clustering of parasites from Zambia and neighboring countries, and distinct clustering of parasites from West Africa. Within Zambia, our identity by descent (IBD) relatedness analysis uncovered spatial clustering of closely related parasite pairs at the local level and rare cases of long-distance sharing. Genomic regions with large shared IBD segments and strong positive selection signatures identified genes involved in sulfadoxine-pyrimethamine and artemisinin combination therapies drug resistance, but no signature related to chloroquine resistance. Together, our findings enhance our understanding of P. falciparum transmission nationwide in Zambia and highlight the urgency of strengthening malaria control programs and surveillance of antimalarial drug resistance.

Mobile Phone Data: A Survey of Techniques, Features, and Applications

Due to the rapid growth in the use of smartphones, the digital traces (e.g., mobile phone data, call detail records) left by the use of these devices have been widely employed to assess and predict human communication behaviors and mobility patterns in various disciplines and domains, such as urban sensing, epidemiology, public transportation, data protection, and criminology. These digital traces provide significant spatiotemporal (geospatial and time-related) data, revealing people's mobility patterns as well as communication (incoming and outgoing calls) data, revealing people's social networks and interactions. Thus, service providers collect smartphone data by recording the details of every user activity or interaction (e.g., making a phone call, sending a text message, or accessing the internet) done using a smartphone and storing these details on their databases. This paper surveys different methods and approaches for assessing and predicting human communication behaviors and mobility patterns from mobile phone data and differentiates them in terms of their strengths and weaknesses. It also gives information about spatial, temporal, and call characteristics that have been extracted from mobile phone data and used to model how people communicate and move. We survey mobile phone data research published between 2013 and 2021 from eight main databases, namely, the ACM Digital Library, IEEE Xplore, MDPI, SAGE, Science Direct, Scopus, SpringerLink, and Web of Science. Based on our inclusion and exclusion criteria, 148 studies were selected.

Climatic, land-use and socio-economic factors can predict malaria dynamics at fine spatial scales relevant to local health actors: Evidence from rural Madagascar

While much progress has been achieved over the last decades, malaria surveillance and control remain a challenge in countries with limited health care access and resources. High-resolution predictions of malaria incidence using routine surveillance data could represent a powerful tool to health practitioners by targeting malaria control activities where and when they are most needed. Here, we investigate the predictors of spatio-temporal malaria dynamics in rural Madagascar, estimated from facility-based passive surveillance data. Specifically, this study integrates climate, land-use, and representative household survey data to explain and predict malaria dynamics at a high spatial resolution (i.e., by Fokontany, a cluster of villages) relevant to health care practitioners. Combining generalized linear mixed models (GLMM) and path analyses, we found that socio-economic, land use and climatic variables are all important predictors of monthly malaria incidence at fine spatial scales, via both direct and indirect effects. In addition, out-of-sample predictions from our model were able to identify 58% of the Fokontany in the top quintile for malaria incidence and account for 77% of the variation in the Fokontany incidence rank. These results suggest that it is possible to build a predictive framework using environmental and social predictors that can be complementary to standard surveillance systems and help inform control strategies by field actors at local scales.

Malaria in Angola: recent progress, challenges and future opportunities using parasite demography studies

Over the past two decades, a considerable expansion of malaria interventions has occurred at the national level in Angola, together with cross-border initiatives and regional efforts in southern Africa. Currently, Angola aims to consolidate malaria control and to accelerate the transition from control to pre-elimination, along with other country members of the Elimination 8 initiative. However, the tremendous heterogeneity in malaria prevalence among Angolan provinces, as well as internal population movements and migration across borders, represent major challenges for the Angolan National Malaria Control Programme. This review aims to contribute to the understanding of factors underlying the complex malaria situation in Angola and to encourage future research studies on transmission dynamics and population structure of Plasmodium falciparum, important areas to complement host epidemiological information and to help reenergize the goal of malaria elimination in the country.

Connecting the dots: understanding how human mobility shapes TB epidemics

Tuberculosis (TB) remains a leading infectious cause of death worldwide. Reducing TB infections and TB-related deaths rests ultimately on stopping forward transmission from infectious to susceptible individuals. Critical to this effort is understanding how human host mobility shapes the transmission and dispersal of new or existing strains of Mycobacterium tuberculosis (Mtb). Important questions remain unanswered. What kinds of mobility, over what temporal and spatial scales, facilitate TB transmission? How do human mobility patterns influence the dispersal of novel Mtb strains, including emergent drug-resistant strains? This review summarizes the current state of knowledge on mobility and TB epidemic dynamics, using examples from three topic areas, including inference of genetic and spatial clustering of infections, delineating source-sink dynamics, and mapping the dispersal of novel TB strains, to examine scientific questions and methodological issues within this topic. We also review new data sources for measuring human mobility, including mobile phone-associated movement data, and discuss important limitations on their use in TB epidemiology.

Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar

For emerging epidemics such as the COVID-19 pandemic, quantifying travel is a key component of developing accurate predictive models of disease spread to inform public health planning. However, in many LMICs, traditional data sets on travel such as commuting surveys as well as non-traditional sources such as mobile phone data are lacking, or, where available, have only rarely been leveraged by the public health community. Evaluating the accuracy of available data to measure transmission-relevant travel may be further hampered by limited reporting of suspected and laboratory confirmed infections. Here, we leverage case data collected as part of a COVID-19 dashboard collated via daily reports from the Malagasy authorities on reported cases of SARS-CoV-2 across the 22 regions of Madagascar. We compare the order of the timing of when cases were reported with predictions from a SARS-CoV-2 metapopulation model of Madagascar informed using various measures of connectivity including a gravity model based on different measures of distance, Internal Migration Flow data, and mobile phone data. Overall, the models based on mobile phone connectivity and the gravity-based on Euclidean distance best predicted the observed spread. The ranks of the regions most remote from the capital were more difficult to predict but interestingly, regions where the mobile phone connectivity model was more accurate differed from those where the gravity model was most accurate. This suggests that there may be additional features of mobility or connectivity that were consistently underestimated using all approaches but are epidemiologically relevant. This work highlights the importance of data availability and strengthening collaboration among different institutions with access to critical data - models are only as good as the data that they use, so building towards effective data-sharing pipelines is essential.

Challenges and opportunities in accessing mobile phone data for COVID-19 response in developing countries

Anonymous and aggregated statistics derived from mobile phone data have proven efficacy as a proxy for human mobility in international development work and as inputs to epidemiological modeling of the spread of infectious diseases such as COVID-19. Despite the widely accepted promise of such data for better development outcomes, challenges persist in their systematic use across countries. This is not only the case for steady-state development use cases such as in the transport or urban development sectors, but also for sudden-onset emergencies such as epidemics in the health sector or natural disasters in the environment sector. This article documents an effort to gain systematized access to and use of anonymized, aggregated mobile phone data across 41 countries, leading to fruitful collaborations in nine developing countries over the course of one year. The research identifies recurring roadblocks and replicable successes, offers lessons learned, and calls for a bold vision for future successes. An emerging model for a future that enables steady-state access to insights derived from mobile big data - such that they are available over time for development use cases - will require investments in coalition building across multiple stakeholders, including local researchers and organizations, awareness raising of various key players, demand generation and capacity building, creation and adoption of standards to facilitate access to data and their ethical use, an enabling regulatory environment and long-term financing schemes to fund these activities.

Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar

For emerging epidemics such as the COVID-19 pandemic, quantifying travel is a key component of developing accurate predictive models of disease spread to inform public health planning. However, in many LMICs, traditional data sets on travel such as commuting surveys as well as non-traditional sources such as mobile phone data are lacking, or, where available, have only rarely been leveraged by the public health community. Evaluating the accuracy of available data to measure transmission-relevant travel may be further hampered by limited reporting of suspected and laboratory confirmed infections. Here, we leverage case data collected as part of a COVID-19 dashboard collated via daily reports from the Malagasy authorities on reported cases of SARS-CoV-2 across the 22 regions of Madagascar. We compare the order of the timing of when cases were reported with predictions from a SARS-CoV-2 metapopulation model of Madagascar informed using various measures of connectivity including a gravity model based on different measures of distance, Internal Migration Flow data, and mobile phone data. Overall, the models based on mobile phone connectivity and the gravity-based on Euclidean distance best predicted the observed spread. The ranks of the regions most remote from the capital were more difficult to predict but interestingly, regions where the mobile phone connectivity model was more accurate differed from those where the gravity model was most accurate. This suggests that there may be additional features of mobility or connectivity that were consistently underestimated using all approaches, but are epidemiologically relevant. This work highlights the importance of data availability and strengthening collaboration among different institutions with access to critical data - models are only as good as the data that they use, so building towards effective data-sharing pipelines is essential.

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.

The Role of Phylogenetics in Discerning HIV-1 Mixing among Vulnerable Populations and Geographic Regions in Sub-Saharan Africa: A Systematic Review

To reduce global HIV-1 incidence, there is a need to understand and disentangle HIV-1 transmission dynamics and to determine the geographic areas and populations that act as hubs or drivers of HIV-1 spread. In Sub-Saharan Africa (sSA), the region with the highest HIV-1 burden, information about such transmission dynamics is sparse. Phylogenetic inference is a powerful method for the study of HIV-1 transmission networks and source attribution. In this review, we assessed available phylogenetic data on mixing between HIV-1 hotspots (geographic areas and populations with high HIV-1 incidence and prevalence) and areas or populations with lower HIV-1 burden in sSA. We searched PubMed and identified and reviewed 64 studies on HIV-1 transmission dynamics within and between risk groups and geographic locations in sSA (published 1995-2021). We describe HIV-1 transmission from both a geographic and a risk group perspective in sSA. Finally, we discuss the challenges facing phylogenetic inference in mixed epidemics in sSA and offer our perspectives and potential solutions to the identified challenges.

Malaria in Southern Venezuela: The hottest hotspot in Latin America

Malaria elimination in Latin America is becoming an elusive goal. Malaria cases reached a historical ~1 million in 2017 and 2018, with Venezuela contributing 53% and 51% of those cases, respectively. Historically, malaria incidence in southern Venezuela has accounted for most of the country's total number of cases. The efficient deployment of disease prevention measures and prediction of disease spread to new regions requires an in-depth understanding of spatial heterogeneity on malaria transmission dynamics. Herein, we characterized the spatial epidemiology of malaria in southern Venezuela from 2007 through 2017 and described the extent to which malaria distribution has changed country-wide over the recent years. We found that disease transmission was focal and more prevalent in the southeast region of southern Venezuela where two persistent hotspots of Plasmodium vivax (76%) and P. falciparum (18%) accounted for ~60% of the total number of cases. Such hotspots are linked to deforestation as a consequence of illegal gold mining activities. Incidence has increased nearly tenfold over the last decade, showing an explosive epidemic growth due to a significant lack of disease control programs. Our findings highlight the importance of spatially oriented interventions to contain the ongoing malaria epidemic in Venezuela. This work also provides baseline epidemiological data to assess cross-border malaria dynamics and advocates for innovative control efforts in the Latin American region.

Reconciling model predictions with low reported cases of COVID-19 in Sub-Saharan Africa: insights from Madagascar

COVID-19 has wreaked havoc globally with particular concerns for sub-Saharan Africa (SSA), where models suggest that the majority of the population will become infected. Conventional wisdom suggests that the continent will bear a higher burden of COVID-19 for the same reasons it suffers from other infectious diseases: ecology, socio-economic conditions, lack of water and sanitation infrastructure, and weak health systems. However, so far SSA has reported lower incidence and fatalities compared to the predictions of standard models and the experience of other regions of the world. There are three leading explanations, each with different implications for the final epidemic burden: (1) low case detection, (2) differences in epidemiology (e.g. low R 0 ), and (3) policy interventions. The low number of cases have led some SSA governments to relaxing these policy interventions. Will this result in a resurgence of cases? To understand how to interpret the lower-than-expected COVID-19 case data in Madagascar, we use a simple age-structured model to explore each of these explanations and predict the epidemic impact associated with them. We show that the incidence of COVID-19 cases as of July 2020 can be explained by any combination of the late introduction of first imported cases, early implementation of non-pharmaceutical interventions (NPIs), and low case detection rates. We then re-evaluate these findings in the context of the COVID-19 epidemic in Madagascar through August 2020. This analysis reinforces that Madagascar, along with other countries in SSA, remains at risk of a growing health crisis. If NPIs remain enforced, up to 50,000 lives may be saved. Even with NPIs, without vaccines and new therapies, COVID-19 could infect up to 30% of the population, making it the largest public health threat in Madagascar for the coming year, hence the importance of clinical trials and continually improving access to healthcare.

Managing the spread of disease with mobile phone data

While human mobility has important benefits for economic growth, it can generate negative externalities. This paper studies the effect of mobility on the spread of disease in a low-incidence setting when people do not internalize their risks to others. Using malaria as a case study and 15 billion mobile phone records across nine million SIM cards, this paper quantifies the relationship between travel and the spread of disease. The estimates indicate that an infected traveler contributes to 1.66 additional cases reported in the health facility at the traveler's destination. This paper develops a simulation-based policy tool that uses mobile phone data to inform strategic targeting of travelers based on their origins and destinations. The simulations suggest that targeting informed by mobile phone data could reduce the caseload by 50 percent more than current strategies that rely only on previous incidence.

Inference and prediction of malaria transmission dynamics using time series data

BACKGROUND

Disease surveillance systems are essential for effective disease intervention and control by monitoring disease prevalence as time series. To evaluate the severity of an epidemic, statistical methods are widely used to forecast the trend, seasonality, and the possible number of infections of a disease. However, most statistical methods are limited in revealing the underlying dynamics of disease transmission, which may be affected by various impact factors, such as environmental, meteorological, and physiological factors. In this study, we focus on investigating malaria transmission dynamics based on time series data.

METHODS

A data-driven nonlinear stochastic model is proposed to infer and predict the dynamics of malaria transmission based on the time series of prevalence data. Specifically, the dynamics of malaria transmission is modeled based on the notion of vectorial capacity (VCAP) and entomological inoculation rate (EIR). A particle Markov chain Monte Carlo (PMCMC) method is employed to estimate the model parameters. Accordingly, a one-step-ahead prediction method is proposed to project the number of future malaria infections. Finally, two case studies are carried out on the inference and prediction of Plasmodium vivax transmission in Tengchong and Longling, Yunnan province, China.

RESULTS

The results show that the trained data-driven stochastic model can well fit the historical time series of P. vivax prevalence data in both counties from 2007 to 2010. Moreover, with well-trained model parameters, the proposed one-step-ahead prediction method can achieve better performances than that of the seasonal autoregressive integrated moving average model with respect to predicting the number of future malaria infections.

CONCLUSIONS

By involving dynamically changing impact factors, the proposed data-driven model together with the PMCMC method can successfully (i) depict the dynamics of malaria transmission, and (ii) achieve accurate one-step-ahead prediction about malaria infections. Such a data-driven method has the potential to investigate malaria transmission dynamics in other malaria-endemic countries/regions.

Mapping the travel patterns of people with malaria in Bangladesh

BACKGROUND

Spread of malaria and antimalarial resistance through human movement present major threats to current goals to eliminate the disease. Bordering the Greater Mekong Subregion, southeast Bangladesh is a potentially important route of spread to India and beyond, but information on travel patterns in this area are lacking.

METHODS

Using a standardised short survey tool, 2090 patients with malaria were interviewed at 57 study sites in 2015-2016 about their demographics and travel patterns in the preceding 2 months.

RESULTS

Most travel was in the south of the study region between Cox's Bazar district (coastal region) to forested areas in Bandarban (31% by days and 45% by nights), forming a source-sink route. Less than 1% of travel reported was between the north and south forested areas of the study area. Farmers (21%) and students (19%) were the top two occupations recorded, with 67 and 47% reporting travel to the forest respectively. Males aged 25-49 years accounted for 43% of cases visiting forests but only 24% of the study population. Children did not travel. Women, forest dwellers and farmers did not travel beyond union boundaries. Military personnel travelled the furthest especially to remote forested areas.

CONCLUSIONS

The approach demonstrated here provides a framework for identifying key traveller groups and their origins and destinations of travel in combination with knowledge of local epidemiology to inform malaria control and elimination efforts. Working with the NMEP, the findings were used to derive a set of policy recommendations to guide targeting of interventions for elimination.

HIV transmission and source-sink dynamics in sub-Saharan Africa

Multiple phylogenetic studies of HIV in sub-Saharan Africa have shown that mobility-driven transmission frequently occurs: many communities export and import strains. Mobility-driven transmission can result in source-sink dynamics: one community can sustain a micro-epidemic in another community in which transmission is too low to be self-sustaining. In epidemiology, the basic reproduction number (R₀) is used to specify the sustainability threshold. R₀ represents the average number of secondary infections generated by one infected individual in a community in which everyone is susceptible. If R₀ is greater than 1, transmission is high enough to sustain an epidemic; if R₀ is less than 1, it is not. Here, we discuss the conditions that are needed (in terms of R₀) for source-sink transmission dynamics to occur in generalised HIV epidemics in sub-Saharan Africa, present an example of where these conditions could occur (ie, Namibia), and discuss the necessity of considering mobility-driven transmission when designing control strategies. Additionally, we discuss the need for a new generation of HIV transmission models that are more realistic than the current models. The new models should reflect not only geographical variation in epidemiology and demography, but also the spatial-temporal complexity of population-level movement patterns.

Mapping malaria seasonality in Madagascar using health facility data

BACKGROUND

Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions. Identifying location-specific seasonality characteristics is useful for planning interventions. While most existing maps of malaria seasonality use fixed thresholds of rainfall, temperature, and/or vegetation indices to identify suitable transmission months, we construct a statistical modelling framework for characterising the seasonal patterns derived directly from monthly health facility data.

METHODS

With data from 2669 of the 3247 health facilities in Madagascar, a spatiotemporal regression model was used to estimate seasonal patterns across the island. In the absence of catchment population estimates or the ability to aggregate to the district level, this focused on the monthly proportions of total annual cases by health facility level. The model was informed by dynamic environmental covariates known to directly influence seasonal malaria trends. To identify operationally relevant characteristics such as the transmission start months and associated uncertainty measures, an algorithm was developed and applied to model realisations. A seasonality index was used to incorporate burden information from household prevalence surveys and summarise 'how seasonal' locations are relative to their surroundings.

RESULTS

Positive associations were detected between monthly case proportions and temporally lagged covariates of rainfall and temperature suitability. Consistent with the existing literature, model estimates indicate that while most parts of Madagascar experience peaks in malaria transmission near March-April, the eastern coast experiences an earlier peak around February. Transmission was estimated to start in southeast districts before southwest districts, suggesting that indoor residual spraying should be completed in the same order. In regions where the data suggested conflicting seasonal signals or two transmission seasons, estimates of seasonal features had larger deviations and therefore less certainty.

CONCLUSIONS

Monthly health facility data can be used to establish seasonal patterns in malaria burden and augment the information provided by household prevalence surveys. The proposed modelling framework allows for evidence-based and cohesive inferences on location-specific seasonal characteristics. As health surveillance systems continue to improve, it is hoped that more of such data will be available to improve our understanding and planning of intervention strategies.

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

RATIONALE FOR REVIEW

The increasing mobility of populations allows pathogens to move rapidly and far, making endemic or epidemic regions more connected to the rest of the world than at any time in history. However, the ability to measure and monitor human mobility, health risk and their changing patterns across spatial and temporal scales using traditional data sources has been limited. To facilitate a better understanding of the use of emerging mobile phone technology and data in travel medicine, we reviewed relevant work aiming at measuring human mobility, disease connectivity and health risk in travellers using mobile geopositioning data.

KEY FINDINGS

Despite some inherent biases of mobile phone data, analysing anonymized positions from mobile users could precisely quantify the dynamical processes associated with contemporary human movements and connectivity of infectious diseases at multiple temporal and spatial scales. Moreover, recent progress in mobile health (mHealth) technology and applications, integrating with mobile positioning data, shows great potential for innovation in travel medicine to monitor and assess real-time health risk for individuals during travel.

CONCLUSIONS

Mobile phones and mHealth have become a novel and tremendously powerful source of information on measuring human movements and origin-destination-specific risks of infectious and non-infectious health issues. The high penetration rate of mobile phones across the globe provides an unprecedented opportunity to quantify human mobility and accurately estimate the health risks in travellers. Continued efforts are needed to establish the most promising uses of these data and technologies for travel health.

A stakeholder workshop about modelled maps of key malaria indicator survey indicators in Madagascar

The Demographic and Health Surveys (DHS) Program has supported three household Malaria Indicator Surveys (MIS) in Madagascar. The results of 13 key malaria indicators from these surveys have been mapped as continuous surfaces using model-based geostatistical methods. The opportunities and limitations of these mapped outputs were discussed during a workshop in Antananarivo, Madagascar in July 2018, attended by 15 representatives from various implementation, policy and research stakeholder institutions in Madagascar. Participants evaluated the findings from the maps, using these to develop figures and narratives to support their work in the control of malaria in Madagascar.

Spatiotemporal analysis of malaria for new sustainable control strategies

Malaria transmission is highly heterogeneous through time and space, and mapping of this heterogeneity is necessary to better understand local dynamics. New targeted policies are needed as numerous countries have placed malaria elimination on their public health agenda for 2030. In this context, developing national health information systems and collecting information at sufficiently precise scales (at least at the 'week' and 'village' scales), is of strategic importance. In a recent study, Macharia et al. relied on extensive prevalence survey data to develop malaria risk maps for Kenya, including uncertainty assessments specifically designed to support decision-making by the National Malaria Control Program. Targeting local persistent transmission or epidemiologic changes is necessary to maintain efficient control, but also to deploy sustainable elimination strategies against identified transmission bottlenecks such as the reservoir of subpatent infections. Such decision-making tools are paramount to allocate resources based on sound scientific evidence and public health priorities.Please see related article: https://malariajournal.biomedcentral.com/articles/10.1186/s12936-018-2489-9 .