Mapping malaria by combining parasite genomic and epidemiologic data

AbstractRecent global progress in scaling up malaria control interventions has revived the goal of complete elimination in many countries. Decreasing transmission intensity generally leads to increasingly patchy spatial patterns of malaria transmission, however, and control programs must accurately identify remaining foci in order to target interventions efficiently. In particular, mosquito control interventions like bed nets and insecticide spraying are best targeted to transmission hotspots, and the role of connectivity between different pockets of local transmission becomes increasingly important since humans are able to move parasites beyond the limits of mosquito dispersal and re-introduce parasites to previously malaria-free regions. Quantifying the connectivity between regions due to human travel, measuring malaria transmission intensity in different areas, and monitoring parasite spatial spread are therefore key issues for policy-makers because they underpin the feasibility of elimination and inform the path to its attainment. To this end, recent efforts have been made to develop new approaches to incorporating human mobility into spatial epidemiological models, for example using mobile phone data, and there has been a surge of interest in collecting spatially informative parasite samples to measure the genomic signatures of parasite connectivity. Due to their complicated life-cycles, Plasmodium parasites pose unique challenges to researchers in this respect and new methods that move beyond traditional phylogenetic and population genetic tools must be developed to harness genetic information effectively. Here, we discuss the spatial epidemiology of malaria in the context of transmission-reduction interventions, and the challenges and promising directions for the development of integrated mapping, modeling, and genomic approaches that leverage disparate data sets to measure both connectivity and transmission.

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Plasmodium PIMMS43 is required for ookinete evasion of the mosquito complement-like response and sporogonic development in the oocyst

10.1101/652115 ◽

2019 ◽

Author(s):

Chiamaka V. Ukegbu ◽

Maria Giorgalli ◽

Sofia Tapanelli ◽

Luisa D.P. Rona ◽

Amie Jaye ◽

...

Keyword(s):

Malaria Transmission ◽

Disease Transmission ◽

Mosquito Control ◽

Surface Protein ◽

Clinical Malaria ◽

Current Control ◽

Bed Nets ◽

Anopheles Coluzzii ◽

Malaria Parasites ◽

Target Disease

AbstractMalaria transmission requires Plasmodium parasites to successfully infect a female Anopheles mosquito, surviving a series of robust innate immune responses. Understanding how parasites evade these responses can highlight new ways to block malaria transmission. We show that ookinete and sporozoite surface protein PIMMS43 is required for Plasmodium ookinete evasion of the Anopheles coluzzii complement-like system and for sporogonic development in the oocyst. Disruption of P. berghei PIMMS43 triggers robust complement activation and ookinete elimination upon mosquito midgut traversal. Silencing the complement-like system restores ookinete-to-oocyst transition. Antibodies that bind PIMMS43 interfere with parasite immune evasion when ingested with the infectious blood meal and significantly reduce the prevalence and intensity of infection. PIMMS43 genetic structure across African P. falciparum populations indicates allelic adaptation to sympatric vector populations. These data significantly add to our understanding of mosquito-parasite interactions and identify PIMMS43 as a target of interventions aiming at malaria transmission blocking.Author summaryMalaria is a devastating disease transmitted among humans through mosquito bites. Mosquito control has significantly reduced clinical malaria cases and deaths in the last decades. However, as mosquito resistance to insecticides is becoming widespread impacting on current control tools, such as insecticide impregnated bed nets and indoor spraying, new interventions are urgently needed, especially those that target disease transmission. Here, we characterize a protein found on the surface of malaria parasites, which serves to evade the mosquito immune system ensuring disease transmission. Neutralization of PIMMS43, either by eliminating it from the parasite genome or by pre-incubating parasites with antibodies that bind to the protein, is shown to inhibit mosquito infection by malaria parasites. Differences in PIMMS43 detected between malaria parasite populations sampled across Africa suggest that these populations have adapted for transmission by different mosquito vectors that are also differentially distributed across the continent. We conclude that interventions targeting PIMMS43 could block malaria parasites inside mosquitoes before they can infect humans.

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Vector composition, abundance, biting patterns and malaria transmission intensity in Madang, Papua New Guinea: assessment after 7 years of an LLIN-based malaria control programme

Malaria Journal ◽

10.1186/s12936-021-04030-4 ◽

2022 ◽

Vol 21 (1) ◽

Author(s):

John B. Keven ◽

Michelle Katusele ◽

Rebecca Vinit ◽

Daniela Rodríguez-Rodríguez ◽

Manuel W. Hetzel ◽

...

Keyword(s):

Malaria Transmission ◽

Papua New Guinea ◽

Malaria Control ◽

New Guinea ◽

Control Programme ◽

Transmission Intensity ◽

Bed Nets ◽

Malaria Control Programme ◽

Malaria Transmission Intensity ◽

Vector Abundance

Abstract Background A malaria control programme based on distribution of long-lasting insecticidal bed nets (LLINs) and artemisinin combination therapy began in Papua New Guinea in 2009. After implementation of the programme, substantial reductions in vector abundance and malaria transmission intensity occurred. The research reported here investigated whether these reductions remained after seven years of sustained effort. Methods All-night (18:00 to 06:00) mosquito collections were conducted using human landing catches and barrier screen methods in four villages of Madang Province between September 2016 and March 2017. Anopheles species identification and sporozoite infection with Plasmodium vivax and Plasmodium falciparum were determined with molecular methods. Vector composition was expressed as the relative proportion of different species in villages, and vector abundance was quantified as the number of mosquitoes per barrier screen-night and per person-night. Transmission intensity was quantified as the number of sporozoite-infective vector bites per person-night. Results Five Anopheles species were present, but vector composition varied greatly among villages. Anopheles koliensis, a strongly anthropophilic species was the most prevalent in Bulal, Matukar and Wasab villages, constituting 63.7–73.8% of all Anopheles, but in Megiar Anopheles farauti was the most prevalent species (97.6%). Vector abundance varied among villages (ranging from 2.8 to 72.3 Anopheles per screen-night and 2.2–31.1 Anopheles per person-night), and spatially within villages. Malaria transmission intensity varied among the villages, with values ranging from 0.03 to 0.5 infective Anopheles bites per person-night. Most (54.1–75.1%) of the Anopheles bites occurred outdoors, with a substantial proportion (25.5–50.8%) occurring before 22:00. Conclusion The estimates of vector abundance and transmission intensity in the current study were comparable to or higher than estimates in the same villages in 2010–2012, indicating impeded programme effectiveness. Outdoor and early biting behaviours of vectors are some of the likely explanatory factors. Heterogeneity in vector composition, abundance and distribution among and within villages challenge malaria control programmes and must be considered when planning them.

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Mapping imported malaria in Bangladesh using parasite genetic and human mobility data

eLife ◽

10.7554/elife.43481 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 22

Author(s):

Hsiao-Han Chang ◽

Amy Wesolowski ◽

Ipsita Sinha ◽

Christopher G Jacob ◽

Ayesha Mahmud ◽

...

Keyword(s):

Mobile Phone ◽

Human Mobility ◽

Genetic Data ◽

Epidemiological Surveillance ◽

Mobile Phone Data ◽

Spatial Spread ◽

Control Programs ◽

Mobility Data ◽

Imported Infections ◽

National Control

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.

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Incorporating human mobility data improves forecasts of Dengue fever in Thailand

Scientific Reports ◽

10.1038/s41598-020-79438-0 ◽

2021 ◽

Vol 11 (1) ◽

Cited By ~ 1

Author(s):

Mathew V. Kiang ◽

Mauricio Santillana ◽

Jarvis T. Chen ◽

Jukka-Pekka Onnela ◽

Nancy Krieger ◽

...

Keyword(s):

Time Series ◽

Dengue Fever ◽

Human Mobility ◽

Epidemiological Data ◽

Early Warning Systems ◽

Environmental Data ◽

Mobile Phone Data ◽

Effective Vaccine ◽

Spatial Spread ◽

Mobility Data

AbstractOver 390 million people worldwide are infected with dengue fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many dengue forecasting approaches have used environmental data linked to mosquito ecology to predict when epidemics will occur, but these have had mixed results. Conversely, human mobility, an important driver in the spatial spread of infection, is often ignored. Here we compare time-series forecasts of dengue fever in Thailand, integrating epidemiological data with mobility models generated from mobile phone data. We show that geographically-distant provinces strongly connected by human travel have more highly correlated dengue incidence than weakly connected provinces of the same distance, and that incorporating mobility data improves traditional time-series forecasting approaches. Notably, no single model or class of model always outperformed others. We propose an adaptive, mosaic forecasting approach for early warning systems.

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Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics

JMIR Research Protocols ◽

10.2196/24432 ◽

2020 ◽

Vol 9 (12) ◽

pp. e24432

Author(s):

Zhenlong Li ◽

Xiaoming Li ◽

Dwayne Porter ◽

Jiajia Zhang ◽

Yuqin Jiang ◽

...

Keyword(s):

Public Health ◽

Big Data ◽

Infectious Diseases ◽

Spatial Scales ◽

Human Mobility ◽

Human Movement ◽

Data Sources ◽

Mobile Phone Data ◽

Spatial Spread ◽

Mobility Data

Background Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). Objective Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). Methods We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. Results This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. Conclusions Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. International Registered Report Identifier (IRRID) DERR1-10.2196/24432

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Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics (Preprint)

10.2196/preprints.24432 ◽

2020 ◽

Author(s):

Zhenlong Li ◽

Xiaoming Li ◽

Dwayne Porter ◽

Jiajia Zhang ◽

Yuqin Jiang ◽

...

Keyword(s):

Public Health ◽

Big Data ◽

Infectious Diseases ◽

Spatial Scales ◽

Human Mobility ◽

Human Movement ◽

Data Sources ◽

Mobile Phone Data ◽

Spatial Spread ◽

Mobility Data

BACKGROUND Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). OBJECTIVE Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). METHODS We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. RESULTS This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. CONCLUSIONS Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/24432

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Effect of malaria transmission reduction by insecticide-treated bed nets (ITNs) on the genetic diversity of Plasmodium falciparum merozoite surface protein (MSP-1) and circumsporozoite (CSP) in western Kenya

Malaria Journal ◽

10.1186/1475-2875-12-295 ◽

2013 ◽

Vol 12 (1) ◽

Cited By ~ 6

Author(s):

Simon K Kariuki ◽

James Njunge ◽

Ann Muia ◽

Geofrey Muluvi ◽

Wangeci Gatei ◽

...

Keyword(s):

Genetic Diversity ◽

Plasmodium Falciparum ◽

Malaria Transmission ◽

Surface Protein ◽

Merozoite Surface Protein ◽

Bed Nets ◽

Western Kenya ◽

Falciparum Merozoite ◽

Transmission Reduction ◽

Insecticide Treated Bed Nets

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Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan

BMC Public Health ◽

10.1186/s12889-021-10260-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Meng-Chun Chang ◽

Rebecca Kahn ◽

Yu-An Li ◽

Cheng-Sheng Lee ◽

Caroline O. Buckee ◽

...

Keyword(s):

Disease Surveillance ◽

Initial Conditions ◽

Human Mobility ◽

Human Movement ◽

Disease Spread ◽

Spatial Spread ◽

Movement Data ◽

Intercity Travel ◽

Travel Restrictions ◽

The Impact

Abstract Background As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. Methods In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. Results We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Conclusions To prepare for the potential spread within Taiwan, we utilized Facebook’s aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

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