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Walshe R, Pongsoipetch K, Mukem S, Kamsri T, Singkham N, Sudathip P, Kitchakarn S, Maude RR, Maude RJ. Assessing receptivity to malaria using case surveillance and forest data in a near-elimination setting in northeast Thailand. Malar J 2024; 23:224. [PMID: 39080748 PMCID: PMC11290226 DOI: 10.1186/s12936-024-05044-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 07/16/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Thailand aimed to eliminate malaria by 2024, and as such is planning for future prevention of re-establishment in malaria free provinces. Understanding the receptivity of local areas to malaria allows the appropriate targeting of interventions. Current approaches to assessing receptivity involve collecting entomological data. Forest coverage is known to be associated with malaria risk, as an environment conducive to both vector breeding and high-risk human behaviours. METHODS Geolocated, anonymized, individual-level surveillance data from 2011 to 2021 from the Thai Division of Vector-Borne Disease (DVBD) was used to calculate incidence and estimated Rc at village level. Forest cover was calculated using raster maps of tree crown cover density and year of forest loss from the publicly available Hansen dataset. Incidence and forest cover were compared graphically and using Spearman's rho. The current foci classification system was applied to data from the last 5 years (2017-2021) and forest cover for 2021 compared between the classifications. A simple risk score was developed to identify villages with high receptivity. RESULTS There was a non-linear decrease in annual cases by 96.6% (1061 to 36) across the two provinces from 2011 to 2021. Indigenous Annual Parasite Index (API) and approximated Rc were higher in villages in highly forested subdistricts, and with higher forest cover within 5 km. Forest cover was also higher in malaria foci which consistently reported malaria cases each year than those which did not. An Rc > 1 was only reported in villages in subdistricts with > 25% forest cover. When applying a simple risk score using forest cover and recent case history, the classifications were comparable to those of the risk stratification system currently used by the DVBD. CONCLUSIONS There was a positive association between forest coverage around a village and indigenous malaria cases. Most local transmission was observed in the heavily forested subdistricts on the international borders with Laos and Cambodia, which are where the most receptive villages are located. These areas are at greater risk of importation of malaria due to population mobility and forest-going activities. Combining forest cover and recent case surveillance data with measures of vulnerability may be useful for prediction of malaria recurrence risk.
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Affiliation(s)
- Rebecca Walshe
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Kulchada Pongsoipetch
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Suwanna Mukem
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Tanong Kamsri
- Phibun Mangsahan Hospital, Phibun, Ubon Ratchathani, Thailand
- Provincial Health Office, Ubon Ratchathani, Thailand
| | | | - Prayuth Sudathip
- Division of Vector Borne Diseases, Department of Disease Control, Tiwanond Road, Nonthaburi, 11000, Thailand
| | - Suravadee Kitchakarn
- Division of Vector Borne Diseases, Department of Disease Control, Tiwanond Road, Nonthaburi, 11000, Thailand
| | | | - Richard James Maude
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- The Open University, Milton Keynes, UK.
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Pongsoipetch K, Walshe R, Mukem S, Kamsri T, Singkham N, Sudathip P, Kitchakarn S, Maude RR, Maude RJ. Mapping malaria transmission foci in Northeast Thailand from 2011 to 2021: approaching elimination in a hypoendemic area. Malar J 2024; 23:212. [PMID: 39020432 PMCID: PMC11253324 DOI: 10.1186/s12936-024-05026-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 06/25/2024] [Indexed: 07/19/2024] Open
Abstract
BACKGROUND Thailand is approaching local elimination of malaria in the eastern provinces. It has successfully reduced the number of cases over the past decade, but there are persistent transmission hot spots in and around forests. This study aimed to use data from the malaria surveillance system to describe the spatiotemporal trends of malaria in Northeast Thailand and fine-scale patterns in locally transmitted cases between 2011 and 2021. METHODS Case data was stratified based on likely location of infection and parasite species. Annual Parasite Index per 1000 population (API) was calculated for different categories. Time series decomposition was performed to identify trends and seasonal patterns. Statistically significant clusters of high (hot spots) and low (cold spots) API were identified using the Getis-Ord Gi* statistic. The stability of those hot spots and the absolute change in the proportion of API density from baseline were compared by case type. RESULTS The total number of confirmed cases experienced a non-linear decline by 96.6%, from 1061 in 2011 to 36 in 2021. There has been a decline in both Plasmodium vivax and Plasmodium falciparum case numbers, with only four confirmed P. falciparum cases over the last two years-a 98.89% drop from 180 in 2011. API was generally higher in Si Sa Ket province, which had peaks every 2-3 years. There was a large outbreak in Ubon Ratchathani in 2014-2016 which had a high proportion of P. falciparum reported. The proportion of cases classified increased over the study period, and the proportion of cases classed as indigenous to the village of residence increased from 0.2% to 33.3%. There were stable hot spots of indigenous and imported cases in the south of Si Sa Ket and southeast of Ubon Ratchathani. Plasmodium vivax hot spots were observed into recent years, while those of P. falciparum decreased to zero in Ubon in 2020 and emerged in the eastern part in 2021, the same year that P. falciparum hot spots in Si Sa Ket reached zero. CONCLUSIONS There has been a large, non-linear decline in the number of malaria cases reported and an increasing proportion of cases are classed as indigenous to the patient's village of residence. Stable hot spots of ongoing transmission in the forested border areas were identified, with transmission likely persisting because of remote location and high-risk forest-going behaviours. Future efforts should include cross-border collaboration and continued targeting of high-risk behaviours to reduce the risk of imported cases seeding local transmission.
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Affiliation(s)
- Kulchada Pongsoipetch
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Rebecca Walshe
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Suwanna Mukem
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Tanong Kamsri
- Phibun Mangsahan Hospital, Ubon Ratchathani, Thailand
- Provincial Health Office, Ubon Ratchathani, Thailand
| | | | - Prayuth Sudathip
- Division of Vector Borne Diseases, Department of Disease Control, Ministry of Public Health, Nonthaburi, 11000, Thailand
| | - Suravadee Kitchakarn
- Division of Vector Borne Diseases, Department of Disease Control, Ministry of Public Health, Nonthaburi, 11000, Thailand
| | | | - Richard James Maude
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- The Open University, Milton Keynes, UK.
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Lubinda J, Bi Y, Haque U, Lubinda M, Hamainza B, Moore AJ. Spatio-temporal monitoring of health facility-level malaria trends in Zambia and adaptive scaling for operational intervention. COMMUNICATIONS MEDICINE 2022; 2:79. [PMID: 35789566 PMCID: PMC9249860 DOI: 10.1038/s43856-022-00144-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 06/15/2022] [Indexed: 12/02/2022] Open
Abstract
Background The spatial and temporal variability inherent in malaria transmission within countries implies that targeted interventions for malaria control in high-burden settings and subnational elimination are a practical necessity. Identifying the spatio-temporal incidence, risk, and trends at different administrative geographies within malaria-endemic countries and monitoring them in near real-time as change occurs is crucial for developing and introducing cost-effective, subnational control and elimination intervention strategies. Methods This study developed intelligent data analytics incorporating Bayesian trend and spatio-temporal Integrated Laplace Approximation models to analyse high-burden over 32 million reported malaria cases from 1743 health facilities in Zambia between 2009 and 2015. Results The results show that at least 5.4 million people live in catchment areas with increasing trends of malaria, covering over 47% of all health facilities, while 5.7 million people live in areas with a declining trend (95% CI), covering 27% of health facilities. A two-scale spatio-temporal trend comparison identified significant differences between health facilities and higher-level districts, and the pattern observed in the southeastern region of Zambia provides the first evidence of the impact of recently implemented localised interventions. Conclusions The results support our recommendation for an adaptive scaling approach when implementing national malaria monitoring, control and elimination strategies and a particular need for stratified subnational approaches targeting high-burden regions with increasing disease trends. Strong clusters along borders with highly endemic countries in the north and south of Zambia underscore the need for coordinated cross-border malaria initiatives and strategies. Malaria is an infectious disease that is widespread in many African countries. Malaria transmission within a country can vary between regions, so tailored interventions for malaria control and elimination targeted to different regions are necessary. To achieve this, it is important to measure and monitor the frequency of malaria infections, its risk, and trends at different geographic administrative scales. This study analysed over 32 million reported malaria cases from 1743 health facilities in Zambia between 2009 and 2015. The results showed an increasing national trend in malaria risk and malaria infection frequency and identified differences between health facility and district trends. These findings support a flexible approach when implementing and expanding national malaria monitoring, control and elimination strategies, especially in areas bordering countries where malaria is widespread, cross-border movement is common, and cross-border initiatives could be beneficial. Lubinda et al. analyse over 32 million health-facility reported malaria cases in Zambia (2009–15) to examine spatially-structured temporal trends. They observe overall increasing trends in risk and rates and highlight the potential benefits of using an adaptive scaling approach in national malaria strategies, and a need for cross-border initiatives.
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Stresman G, DePina AJ, Nelli L, Monteiro DDS, Leal SDV, Moreira AL, Furtado UD, Roka JCL, Neatherlin J, Gomes C, Tfeil AK, Lindblade KA. Factors related to human-vector contact that modify the likelihood of malaria transmission during a contained Plasmodium falciparum outbreak in Praia, Cabo Verde. FRONTIERS IN EPIDEMIOLOGY 2022; 2:1031230. [PMID: 38455281 PMCID: PMC10910924 DOI: 10.3389/fepid.2022.1031230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 11/10/2022] [Indexed: 03/09/2024]
Abstract
Background Determining the reproductive rate and how it varies over time and space (RT) provides important insight to understand transmission of a given disease and inform optimal strategies for controlling or eliminating it. Estimating RT for malaria is difficult partly due to the widespread use of interventions and immunity to disease masking incident infections. A malaria outbreak in Praia, Cabo Verde in 2017 provided a unique opportunity to estimate RT directly, providing a proxy for the intensity of vector-human contact and measure the impact of vector control measures. Methods Out of 442 confirmed malaria cases reported in 2017 in Praia, 321 (73%) were geolocated and informed this analysis. RT was calculated using the joint likelihood of transmission between two cases, based on the time (serial interval) and physical distance (spatial interval) between them. Log-linear regression was used to estimate factors associated with changes in RT, including the impact of vector control interventions. A geostatistical model was developed to highlight areas receptive to transmission where vector control activities could be focused in future to prevent or interrupt transmission. Results The RT from individual cases ranged between 0 and 11 with a median serial- and spatial-interval of 34 days [interquartile range (IQR): 17-52] and 1,347 m (IQR: 832-1,985 m), respectively. The number of households receiving indoor residual spraying (IRS) 4 weeks prior was associated with a reduction in RT by 0.84 [95% confidence interval (CI) 0.80-0.89; p-value <0.001] in the peak-and post-epidemic compared to the pre-epidemic period. Conclusions Identifying the effect of reduced human-vector contact through IRS is essential to determining optimal intervention strategies that modify the likelihood of malaria transmission and can inform optimal intervention strategies to accelerate time to elimination. The distance within which two cases are plausibly linked is important for the potential scale of any reactive interventions as well as classifying infections as imported or introduced and confirming malaria elimination.
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Affiliation(s)
- Gillian Stresman
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- College of Public Health, University of South Florida, Tampa, FL, United States
| | | | - Luca Nelli
- School of Biodiversity, One Health, and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
| | | | - Silvânia da Veiga Leal
- Laboratório de Entomologia Médica, Instituto Nacional de Saúde Pública, Praia, Cabo Verde
| | | | | | | | - John Neatherlin
- U.S. Centers for Disease Control and Prevention, Atlanta, GA, United States
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Byrne I, Cramer E, Nelli L, Rerolle F, Wu L, Patterson C, Rosado J, Dumont E, Tetteh KKA, Dantzer E, Hongvanthong B, Fornace KM, Stresman G, Lover A, Bennett A, Drakeley C. Characterizing the spatial distribution of multiple malaria diagnostic endpoints in a low-transmission setting in Lao PDR. Front Med (Lausanne) 2022; 9:929366. [PMID: 36059850 PMCID: PMC9433740 DOI: 10.3389/fmed.2022.929366] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 07/22/2022] [Indexed: 11/21/2022] Open
Abstract
The epidemiology of malaria changes as prevalence falls in low-transmission settings, with remaining infections becoming more difficult to detect and diagnose. At this stage active surveillance is critical to detect residual hotspots of transmission. However, diagnostic tools used in active surveillance generally only detect concurrent infections, and surveys may benefit from sensitive tools such as serological assays. Serology can be used to interrogate and characterize individuals' previous exposure to malaria over longer durations, providing information essential to the detection of remaining foci of infection. We ran blood samples collected from a 2016 population-based survey in the low-transmission setting of northern Lao PDR on a multiplexed bead assay to characterize historic and recent exposures to Plasmodium falciparum and vivax. Using geostatistical methods and remote-sensing data we assessed the environmental and spatial associations with exposure, and created predictive maps of exposure within the study sites. We additionally linked the active surveillance PCR and serology data with passively collected surveillance data from health facility records. We aimed to highlight the added information which can be gained from serology as a tool in active surveillance surveys in low-transmission settings, and to identify priority areas for national surveillance programmes where malaria risk is higher. We also discuss the issues faced when linking malaria data from multiple sources using multiple diagnostic endpoints.
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Affiliation(s)
- Isabel Byrne
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
- *Correspondence: Isabel Byrne
| | - Estee Cramer
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts-Amherst, Amherst, MA, United States
| | - Luca Nelli
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Francois Rerolle
- Malaria Elimination Initiative, The Global Health Group, University of California, San Francisco, San Francisco, CA, United States
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States
| | - Lindsey Wu
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Catriona Patterson
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Jason Rosado
- Unit of Malaria: Parasites and Hosts, Institut Pasteur, Paris, France
- Infectious Diseases Epidemiology and Analytics G5 Unit, Institut Pasteur, Paris, France
| | - Elin Dumont
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Kevin K. A. Tetteh
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Emily Dantzer
- Malaria Elimination Initiative, The Global Health Group, University of California, San Francisco, San Francisco, CA, United States
| | - Bouasy Hongvanthong
- Center for Malariology, Parasitology and Entomology, Ministry of Health, Vientiane, Laos
| | - Kimberley M. Fornace
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, United Kingdom
| | - Gillian Stresman
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Andrew Lover
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts-Amherst, Amherst, MA, United States
| | - Adam Bennett
- Malaria Elimination Initiative, The Global Health Group, University of California, San Francisco, San Francisco, CA, United States
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States
| | - Chris Drakeley
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
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Dlamini SN, Fall IS, Mabaso SD. Bayesian Geostatistical Modeling to Assess Malaria Seasonality and Monthly Incidence Risk in Eswatini. J Epidemiol Glob Health 2022; 12:340-361. [PMID: 35976542 PMCID: PMC9382628 DOI: 10.1007/s44197-022-00054-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/06/2022] [Indexed: 11/30/2022] Open
Abstract
Eswatini is on the brink of malaria elimination and had however, had to shift its target year to eliminate malaria on several occasions since 2015 as the country struggled to achieve its zero malaria goal. We conducted a Bayesian geostatistical modeling study using malaria case data. A Bayesian distributed lags model (DLM) was implemented to assess the effects of seasonality on cases. A second Bayesian model based on polynomial distributed lags was implemented on the dataset to improve understanding of the lag effect of environmental factors on cases. Results showed that malaria increased during the dry season with proportion 0.051 compared to the rainy season with proportion 0.047 while rainfall of the preceding month (Lag2) had negative effect on malaria as it decreased by proportion − 0.25 (BCI: − 0.46, − 0.05). Night temperatures of the preceding first and second month were significantly associated with increased malaria in the following proportions: at Lag1 0.53 (BCI: 0.23, 0.84) and at Lag2 0.26 (BCI: 0.01, 0.51). Seasonality was an important predictor of malaria with proportion 0.72 (BCI: 0.40, 0.98). High malaria rates were identified for the months of July to October, moderate rates in the months of November to February and low rates in the months of March to June. The maps produced support-targeted malaria control interventions. The Bayesian geostatistical models could be extended for short-term and long-term forecasting of malaria supporting-targeted response both in space and time for effective elimination.
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Affiliation(s)
- Sabelo Nick Dlamini
- Department of Geography, University of Eswatini, Kwaluseni, Manzini, M200, Eswatini. .,World Health Organization, 27 Geneva, Geneva, Switzerland.
| | | | - Sizwe Doctor Mabaso
- Department of Geography, University of Eswatini, Kwaluseni, Manzini, M200, Eswatini
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Yukich JO, Lindblade K, Kolaczinski J. Receptivity to malaria: meaning and measurement. Malar J 2022; 21:145. [PMID: 35527264 PMCID: PMC9080212 DOI: 10.1186/s12936-022-04155-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 04/07/2022] [Indexed: 01/13/2023] Open
Abstract
"Receptivity" to malaria is a construct developed during the Global Malaria Eradication Programme (GMEP) era. It has been defined in varied ways and no consistent, quantitative definition has emerged over the intervening decades. Despite the lack of consistency in defining this construct, the idea that some areas are more likely to sustain malaria transmission than others has remained important in decision-making in malaria control, planning for malaria elimination and guiding activities during the prevention of re-establishment (POR) period. This manuscript examines current advances in methods of measurement. In the context of a decades long decline in global malaria transmission and an increasing number of countries seeking to eliminate malaria, understanding and measuring malaria receptivity has acquired new relevance.
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Affiliation(s)
- Joshua O. Yukich
- grid.265219.b0000 0001 2217 8588Department of Tropical Medicine, Center for Applied Malaria Research and Evaluation, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA USA
| | - Kim Lindblade
- grid.3575.40000000121633745Global Malaria Programme, World Health Organization, Geneva, CH USA
| | - Jan Kolaczinski
- grid.3575.40000000121633745Global Malaria Programme, World Health Organization, Geneva, CH USA
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Assessment of Climate-Driven Variations in Malaria Transmission in Senegal Using the VECTRI Model. ATMOSPHERE 2022. [DOI: 10.3390/atmos13030418] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Several vector-borne diseases, such as malaria, are sensitive to climate and weather conditions. When unusual conditions prevail, for example, during periods of heavy rainfall, mosquito populations can multiply and trigger epidemics. This study, which consists of better understanding the link between malaria transmission and climate factors at a national level, aims to validate the VECTRI model (VECtor borne disease community model of ICTP, TRIeste) in Senegal. The VECTRI model is a grid-distributed dynamical model that couples a biological model for the vector and parasite life cycles to a simple compartmental Susceptible-Exposed-Infectious-Recovered (SEIR) representation of the disease progression in the human host. In this study, a VECTRI model driven by reanalysis data (ERA-5) was used to simulate malaria parameters, such as the entomological inoculation rate (EIR) in Senegal. Observed malaria data from the National Malaria Control Program in Senegal (PNLP/Programme National de Lutte contre le Paludisme au Senegal) and outputs from the climate data used in this study were compared. The findings highlight the unimodal shape of temporal malaria occurrence, and the seasonal malaria transmission contrast is closely linked to the latitudinal variation of the rainfall, showing a south–north gradient over Senegal. This study showed that the peak of malaria takes place from September to October, with a lag of about one month from the peak of rainfall in Senegal. There is an agreement between observations and simulations about decreasing malaria cases on time. These results indicate that the southern area of Senegal is at the highest risk of malaria spread outbreaks. The findings in the paper are expected to guide community-based early-warning systems and adaptation strategies in Senegal, which will feed into the national malaria prevention, response, and care strategies adapted to the needs of local communities.
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Huber JH, Hsiang MS, Dlamini N, Murphy M, Vilakati S, Nhlabathi N, Lerch A, Nielsen R, Ntshalintshali N, Greenhouse B, Perkins TA. Inferring person-to-person networks of Plasmodium falciparum transmission: are analyses of routine surveillance data up to the task? Malar J 2022; 21:58. [PMID: 35189905 PMCID: PMC8860266 DOI: 10.1186/s12936-022-04072-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 01/31/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Inference of person-to-person transmission networks using surveillance data is increasingly used to estimate spatiotemporal patterns of pathogen transmission. Several data types can be used to inform transmission network inferences, yet the sensitivity of those inferences to different data types is not routinely evaluated. METHODS The influence of different combinations of spatial, temporal, and travel-history data on transmission network inferences for Plasmodium falciparum malaria were evaluated. RESULTS The information content of these data types may be limited for inferring person-to-person transmission networks and may lead to an overestimate of transmission. Only when outbreaks were temporally focal or travel histories were accurate was the algorithm able to accurately estimate the reproduction number under control, Rc. Applying this approach to data from Eswatini indicated that inferences of Rc and spatiotemporal patterns therein depend upon the choice of data types and assumptions about travel-history data. CONCLUSIONS These results suggest that transmission network inferences made with routine malaria surveillance data should be interpreted with caution.
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Affiliation(s)
- John H Huber
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA.
| | - Michelle S Hsiang
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Malaria Elimination Initiative, Global Health Group, University of California, San Francisco, CA, USA.,Department of Pediatrics, University of California, San Francisco,, CA, USA
| | - Nomcebo Dlamini
- National Malaria Elimination Programme, Ministry of Health, Manzini, Eswatini
| | - Maxwell Murphy
- Department of Medicine, University of California, San Francisco, CA, USA
| | | | - Nomcebo Nhlabathi
- National Malaria Elimination Programme, Ministry of Health, Manzini, Eswatini
| | - Anita Lerch
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
| | - Rasmus Nielsen
- Department of Integrative Biology and Statistics, University of California, Berkeley, CA, USA
| | | | - Bryan Greenhouse
- Department of Medicine, University of California, San Francisco, CA, USA.,Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - T Alex Perkins
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA.
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Routledge I, Unwin HJT, Bhatt S. Inference of malaria reproduction numbers in three elimination settings by combining temporal data and distance metrics. Sci Rep 2021; 11:14495. [PMID: 34262054 PMCID: PMC8280212 DOI: 10.1038/s41598-021-93238-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 06/11/2021] [Indexed: 11/10/2022] Open
Abstract
Individual-level geographic information about malaria cases, such as the GPS coordinates of residence or health facility, is often collected as part of surveillance in near-elimination settings, but could be more effectively utilised to infer transmission dynamics, in conjunction with additional information such as symptom onset time and genetic distance. However, in the absence of data about the flow of parasites between populations, the spatial scale of malaria transmission is often not clear. As a result, it is important to understand the impact of varying assumptions about the spatial scale of transmission on key metrics of malaria transmission, such as reproduction numbers. We developed a method which allows the flexible integration of distance metrics (such as Euclidian distance, genetic distance or accessibility matrices) with temporal information into a single inference framework to infer malaria reproduction numbers. Twelve scenarios were defined, representing different assumptions about the likelihood of transmission occurring over different geographic distances and likelihood of missing infections (as well as high and low amounts of uncertainty in this estimate). These scenarios were applied to four individual level datasets from malaria eliminating contexts to estimate individual reproduction numbers and how they varied over space and time. Model comparison suggested that including spatial information improved models as measured by second order AIC (ΔAICc), compared to time only results. Across scenarios and across datasets, including spatial information tended to increase the seasonality of temporal patterns in reproduction numbers and reduced noise in the temporal distribution of reproduction numbers. The best performing parameterisations assumed long-range transmission (> 200 km) was possible. Our approach is flexible and provides the potential to incorporate other sources of information which can be converted into distance or adjacency matrices such as travel times or molecular markers.
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Rodriguez MH. Residual Malaria: Limitations of Current Vector Control Strategies to Eliminate Transmission in Residual Foci. J Infect Dis 2021; 223:S55-S60. [PMID: 33906220 PMCID: PMC8079132 DOI: 10.1093/infdis/jiaa582] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
The transmission of Plasmodium parasites in residual foci is currently a major roadblock for malaria elimination. Human activities and behavior, along with outdoor biting mosquitoes with opportunistic feeding preferences are the main causes of the inefficacy of the main vector control interventions, long lasting insecticide-impregnated nets and insecticide residual spraying. Several strategies to abate or repel outdoor biting mosquito vectors are currently being researched, but the impact of insecticide resistance on the efficacy of these and current indoor-applied insecticides requires further assessment. Understanding the human, ecological and vector factors, determining transmission in residual foci is necessary for the design and implementation of novel control strategies. Vector control alone is insufficient without adequate epidemiological surveillance and prompt treatment of malaria cases, the participation of endemic communities in prevention and control is required. In addition, malaria control programs should optimize their structure and organization, and their coordination with other government sectors.
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Affiliation(s)
- Mario H Rodriguez
- Centro de Investigaciones Sobre Enfermedades Infecciosas, Instituto Nacional de Salud Pública, Cuernavaca, Morelos, Mexico
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12
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Unwin HJT, Routledge I, Flaxman S, Rizoiu MA, Lai S, Cohen J, Weiss DJ, Mishra S, Bhatt S. Using Hawkes Processes to model imported and local malaria cases in near-elimination settings. PLoS Comput Biol 2021; 17:e1008830. [PMID: 33793564 PMCID: PMC8043404 DOI: 10.1371/journal.pcbi.1008830] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 04/13/2021] [Accepted: 02/23/2021] [Indexed: 01/29/2023] Open
Abstract
Developing new methods for modelling infectious diseases outbreaks is important for monitoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are well founded mathematical methods that enable us to combine the benefits of both statistical and mechanistic models to recreate and forecast disease transmission beyond just malaria outbreak scenarios. These methods have been successfully used in numerous applications such as social media and earthquake modelling, but are not yet widespread in epidemiology. By using domain-specific knowledge, we can both recreate transmission curves for malaria in China and Eswatini and disentangle the proportion of cases which are imported from those that are community based.
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Affiliation(s)
- H. Juliette T. Unwin
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College, London, United Kingdom
| | - Isobel Routledge
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College, London, United Kingdom
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Seth Flaxman
- Department of Mathematics, Imperial College, London, United Kingdom
| | | | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - Justin Cohen
- Clinton Health Access Initiative, Boston, Massachusetts, United States of America
| | - Daniel J. Weiss
- Malaria Atlas Project, Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Telethon Kids Institute, Perth Children’s Hospital, Nedlands, Western Australia, Australia
- Curtin University, Bentley, Western Australia, Australia
| | - Swapnil Mishra
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College, London, United Kingdom
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College, London, United Kingdom
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13
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Zheng J, Shi B, Xia S, Yang G, Zhou XN. Spatial patterns of <em>Plasmodium vivax</em> transmission explored by multivariate auto-regressive state-space modelling - A case study in Baoshan Prefecture in southern China. GEOSPATIAL HEALTH 2021; 16. [PMID: 33733649 DOI: 10.4081/gh.2021.879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 08/21/2020] [Indexed: 06/12/2023]
Abstract
The transition from the control phase to elimination of malaria in China through the national malaria elimination programme has focussed attention on the need for improvement of the surveillance- response systems. It is now understood that routine passive surveillance is inadequate in the parasite elimination phase that requires supplementation by active surveillance in foci where cluster cases have occurred. This study aims to explore the spatial clusters and temporal trends of malaria cases by the multivariate auto-regressive state-space model (MARSS) along the border to Myanmar in southern China. Data for indigenous cases spanning the period from 2007 to 2010 were extracted from the China's Infectious Diseases Information Reporting Management System (IDIRMS). The best MARSS model indicated that malaria transmission in the study area during 36 months could be grouped into three clusters. The estimation of malaria transmission patterns showed a downward trend across all clusters. The proposed methodology used in this study offers a simple and rapid, yet effective way to categorize patterns of foci which provide assistance for active monitoring of malaria in the elimination phase.
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Affiliation(s)
- Jinxin Zheng
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China; Key Laboratory of Parasite and Vector Biology, National Health Commission, Shanghai, China; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China; Chinese Center for Tropical Diseases Research, Shanghai.
| | - Benyun Shi
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, Jiangsu.
| | - Shang Xia
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China; Key Laboratory of Parasite and Vector Biology, National Health Commission, Shanghai, China; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China; Chinese Center for Tropical Diseases Research, Shanghai.
| | - Guojing Yang
- Hainan Medical University, Laboratory of Tropical Environment and Health, Haikou, Hainan, China; Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute; University of Basel, Basel.
| | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, China; Key Laboratory of Parasite and Vector Biology, National Health Commission, Shanghai, China; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China; Chinese Center for Tropical Diseases Research, Shanghai.
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14
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Mayfield HJ, Sturrock H, Arnold BF, Andrade-Pacheco R, Kearns T, Graves P, Naseri T, Thomsen R, Gass K, Lau CL. Supporting elimination of lymphatic filariasis in Samoa by predicting locations of residual infection using machine learning and geostatistics. Sci Rep 2020; 10:20570. [PMID: 33239779 PMCID: PMC7689447 DOI: 10.1038/s41598-020-77519-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 11/11/2020] [Indexed: 11/26/2022] Open
Abstract
The global elimination of lymphatic filariasis (LF) is a major focus of the World Health Organization. One key challenge is locating residual infections that can perpetuate the transmission cycle. We show how a targeted sampling strategy using predictions from a geospatial model, combining random forests and geostatistics, can improve the sampling efficiency for identifying locations with high infection prevalence. Predictions were made based on the household locations of infected persons identified from previous surveys, and environmental variables relevant to mosquito density. Results show that targeting sampling using model predictions would have allowed 52% of infections to be identified by sampling just 17.7% of households. The odds ratio for identifying an infected individual in a household at a predicted high risk compared to a predicted low risk location was 10.2 (95% CI 4.2-22.8). This study provides evidence that a 'one size fits all' approach is unlikely to yield optimal results when making programmatic decisions based on model predictions. Instead, model assumptions and definitions should be tailored to each situation based on the objective of the surveillance program. When predictions are used in the context of the program objectives, they can result in a dramatic improvement in the efficiency of locating infected individuals.
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Affiliation(s)
- Helen J Mayfield
- Research School of Population Health, Australian National University, Canberra, Australia.
| | - Hugh Sturrock
- Global Health Group, University of California, San Francisco, San Francisco, USA
| | - Benjamin F Arnold
- Proctor Foundation, University of California, San Francisco, San Francisco, USA
| | | | - Therese Kearns
- Menzies School of Health Research, Charles Darwin University, Brisbane, Australia
| | - Patricia Graves
- College of Public Health, Medical and Veterinary Sciences, James Cook University, Cairns, QLD, Australia
| | | | | | - Katherine Gass
- Neglected Tropical Diseases Support Center, Task Force for Global Heath, Decatur, GA, USA
| | - Colleen L Lau
- Research School of Population Health, Australian National University, Canberra, Australia
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15
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Roh ME, Tessema SK, Murphy M, Nhlabathi N, Mkhonta N, Vilakati S, Ntshalintshali N, Saini M, Maphalala G, Chen A, Wilheim J, Prach L, Gosling R, Kunene S, S Hsiang M, Greenhouse B. High Genetic Diversity of Plasmodium falciparum in the Low-Transmission Setting of the Kingdom of Eswatini. J Infect Dis 2020; 220:1346-1354. [PMID: 31190073 PMCID: PMC6743842 DOI: 10.1093/infdis/jiz305] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 06/12/2019] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND To better understand transmission dynamics, we characterized Plasmodium falciparum genetic diversity in Eswatini, where transmission is low and sustained by importation. METHODS Twenty-six P. falciparum microsatellites were genotyped in 66% of confirmed cases (2014-2016; N = 582). Population and within-host diversity were used to characterize differences between imported and locally acquired infections. Logistic regression was used to assess the added value of diversity metrics to classify imported and local infections beyond epidemiology data alone. RESULTS Parasite population in Eswatini was highly diverse (expected heterozygosity [HE] = 0.75) and complex: 67% polyclonal infections, mean multiplicity of infection (MOI) 2.2, and mean within-host infection fixation index (FWS) 0.84. Imported cases had comparable diversity to local cases but exhibited higher MOI (2.4 vs 2.0; P = .004) and lower mean FWS (0.82 vs 0.85; P = .03). Addition of MOI and FWS to multivariate analyses did not increase discrimination between imported and local infections. CONCLUSIONS In contrast to the common perception that P. falciparum diversity declines with decreasing transmission intensity, Eswatini isolates exhibited high parasite diversity consistent with high rates of malaria importation and limited local transmission. Estimates of malaria transmission intensity from genetic data need to consider the effect of importation, especially as countries near elimination.
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Affiliation(s)
- Michelle E Roh
- Malaria Elimination Initiative, Institute of Global Health Sciences, University of California, San Francisco.,Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Sofonias K Tessema
- Division of HIV, Infectious Diseases, and Global Medicine, Department of Medicine, University of California, San Francisco
| | - Maxwell Murphy
- Division of HIV, Infectious Diseases, and Global Medicine, Department of Medicine, University of California, San Francisco
| | | | | | | | | | - Manik Saini
- Clinton Health Access Initiative, Mbabane, Eswatini
| | | | - Anna Chen
- Division of HIV, Infectious Diseases, and Global Medicine, Department of Medicine, University of California, San Francisco
| | - Jordan Wilheim
- Division of HIV, Infectious Diseases, and Global Medicine, Department of Medicine, University of California, San Francisco
| | - Lisa Prach
- Malaria Elimination Initiative, Institute of Global Health Sciences, University of California, San Francisco
| | - Roly Gosling
- Malaria Elimination Initiative, Institute of Global Health Sciences, University of California, San Francisco.,Department of Epidemiology and Biostatistics, University of California, San Francisco
| | | | - Michelle S Hsiang
- Malaria Elimination Initiative, Institute of Global Health Sciences, University of California, San Francisco.,Department of Pediatrics, University of California, San Francisco.,Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas
| | - Bryan Greenhouse
- Division of HIV, Infectious Diseases, and Global Medicine, Department of Medicine, University of California, San Francisco.,Chan Zuckerberg Biohub, San Francisco, California
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16
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Mosnier E, Roux E, Cropet C, Lazrek Y, Moriceau O, Gaillet M, Mathieu L, Nacher M, Demar M, Odonne G, Douine M, Michaud C, Pelleau S, Djossou F, Musset L. Prevalence of Plasmodium spp. in the Amazonian Border Context (French Guiana-Brazil): Associated Factors and Spatial Distribution. Am J Trop Med Hyg 2020; 102:130-141. [PMID: 31769403 PMCID: PMC6947805 DOI: 10.4269/ajtmh.19-0378] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
To implement future malaria elimination strategies in French Guiana, a characterization of the infectious reservoir is recommended. A cross-sectional survey was conducted between October and December 2017 in the French Guianese municipality of St Georges de l'Oyapock, located along the Brazilian border. The prevalence of Plasmodium spp. was determined using a rapid diagnostic test (RDT) and a polymerase chain reaction (PCR). Demographic, house locations, medical history, and biological data were analyzed. Factors associated with Plasmodium spp. carriage were analyzed using logistic regression, and the carriage localization was investigated through spatial cluster analysis. Of the 1,501 samples analyzed with PCR, positive results totaled 90 and 10 for Plasmodium vivax and Plasmodium falciparum, respectively. The general PCR prevalence was 6.6% [5.3-7.9], among which 74% were asymptomatic. Only 13/1,549 were positive by RDT. In multivariate analysis, participants older than 15 years, living in a remote neighborhood, with a prior history of malaria, anemia, and thrombocytopenia were associated with an increased odds of Plasmodium spp. carriage. High-risk clusters of P. vivax carriage were detected in the most remote neighborhoods on the village outskirts and two small foci in the village center. We also detected a hot spot for both P. vivax and P. falciparum symptomatic carriers in the northwestern part of the village. The present study confirms a wide-scale presence of asymptomatic P. falciparum and P. vivax carriers in this area. Although they were more often located in remote areas, their geographic distribution was spatially heterogeneous and complex.
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Affiliation(s)
- Emilie Mosnier
- Ecosystèmes Amazoniens et Pathologie Tropicale, EA3593, Université de Guyane, Cayenne, France.,Pôle des Centres Délocalisés de Prévention et de Soins, Centre Hospitalier Andrée Rosemon, Cayenne, France
| | - Emmanuel Roux
- LIS, ICICT, Fiocruz, Rio de Janeiro, Brazil.,ESPACE-DEV, IRD, Université de Montpellier, Université de La Réunion, Université de Guyane, Université des Antilles, Montpellier, France
| | - Claire Cropet
- Centre d'Investigation Clinique Antilles Guyane-Inserm 1424, Cayenne, France
| | - Yassamine Lazrek
- Laboratoire de Parasitologie, Centre National de Référence du Paludisme, Pôle Zones Endémiques, WHO Collaborating Center for Surveillance of Antimalarial Drug Resistance, Institut Pasteur de la Guyane, Cayenne, France
| | - Olivier Moriceau
- Laboratoire de Parasitologie, Centre National de Référence du Paludisme, Pôle Zones Endémiques, WHO Collaborating Center for Surveillance of Antimalarial Drug Resistance, Institut Pasteur de la Guyane, Cayenne, France
| | - Mélanie Gaillet
- Pôle des Centres Délocalisés de Prévention et de Soins, Centre Hospitalier Andrée Rosemon, Cayenne, France
| | - Luana Mathieu
- Laboratoire de Parasitologie, Centre National de Référence du Paludisme, Pôle Zones Endémiques, WHO Collaborating Center for Surveillance of Antimalarial Drug Resistance, Institut Pasteur de la Guyane, Cayenne, France
| | - Mathieu Nacher
- Centre d'Investigation Clinique Antilles Guyane-Inserm 1424, Cayenne, France
| | - Magalie Demar
- Laboratoire de Parasitologie et Mycologie, Centre Hospitalier Andrée Rosemon, Cayenne, France
| | - Guillaume Odonne
- UMSR Laboratoire Ecologie, Evolution, Interactions des Systèmes Amazoniens Laboratoire Ecologie, Evolution, Interactions des Systèmes Amazoniens (LEESIA), Centre National de la Recherche Scientifique (CNRS), Cayenne, France
| | - Maylis Douine
- Centre d'Investigation Clinique Antilles Guyane-Inserm 1424, Cayenne, France.,Ecosystèmes Amazoniens et Pathologie Tropicale, EA3593, Université de Guyane, Cayenne, France
| | - Céline Michaud
- Pôle des Centres Délocalisés de Prévention et de Soins, Centre Hospitalier Andrée Rosemon, Cayenne, France
| | - Stéphane Pelleau
- Laboratoire de Parasitologie, Centre National de Référence du Paludisme, Pôle Zones Endémiques, WHO Collaborating Center for Surveillance of Antimalarial Drug Resistance, Institut Pasteur de la Guyane, Cayenne, France
| | - Félix Djossou
- Unité de Maladies Infectieuses et Tropicales, Centre Hospitalier Andrée Rosemon, Cayenne, France
| | - Lise Musset
- Laboratoire de Parasitologie, Centre National de Référence du Paludisme, Pôle Zones Endémiques, WHO Collaborating Center for Surveillance of Antimalarial Drug Resistance, Institut Pasteur de la Guyane, Cayenne, France
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17
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Routledge I, Lai S, Battle KE, Ghani AC, Gomez-Rodriguez M, Gustafson KB, Mishra S, Unwin J, Proctor JL, Tatem AJ, Li Z, Bhatt S. Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach. PLoS Comput Biol 2020; 16:e1007707. [PMID: 32203520 PMCID: PMC7117777 DOI: 10.1371/journal.pcbi.1007707] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 04/02/2020] [Accepted: 02/03/2020] [Indexed: 01/02/2023] Open
Abstract
In order to monitor progress towards malaria elimination, it is crucial to be able to measure changes in spatio-temporal transmission. However, common metrics of malaria transmission such as parasite prevalence are under powered in elimination contexts. China has achieved major reductions in malaria incidence and is on track to eliminate, having reporting zero locally-acquired malaria cases in 2017 and 2018. Understanding the spatio-temporal pattern underlying this decline, especially the relationship between locally-acquired and imported cases, can inform efforts to maintain elimination and prevent re-emergence. This is particularly pertinent in Yunnan province, where the potential for local transmission is highest. Using a geo-located individual-level dataset of cases recorded in Yunnan province between 2011 and 2016, we introduce a novel Bayesian framework to model a latent diffusion process and estimate the joint likelihood of transmission between cases and the number of cases with unobserved sources of infection. This is used to estimate the case reproduction number, Rc. We use these estimates within spatio-temporal geostatistical models to map how transmission varied over time and space, estimate the timeline to elimination and the risk of resurgence. We estimate the mean Rc between 2011 and 2016 to be 0.171 (95% CI = 0.165, 0.178) for P. vivax cases and 0.089 (95% CI = 0.076, 0.103) for P. falciparum cases. From 2014 onwards, no cases were estimated to have a Rc value above one. An unobserved source of infection was estimated to be moderately likely (p>0.5) for 19/ 611 cases and high (p>0.8) for 2 cases, suggesting very high levels of case ascertainment. Our estimates suggest that, maintaining current intervention efforts, Yunnan is unlikely to experience sustained local transmission up to 2020. However, even with a mean of 0.005 projected up to 2020, locally-acquired cases are possible due to high levels of importation.
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Affiliation(s)
| | - Shengjie Lai
- University of Southampton, Southampton, United Kingdom
| | | | | | | | - Kyle B. Gustafson
- Institute for Disease Modelling, Bellevue, Washington, United States of America
| | | | | | - Joshua L. Proctor
- Institute for Disease Modelling, Bellevue, Washington, United States of America
| | | | - Zhongjie Li
- Chinese Centers for Disease Control and Prevention, Beijing, China
| | - Samir Bhatt
- Imperial College London, London, United Kingom
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18
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Surendra H, Supargiyono, Ahmad RA, Kusumasari RA, Rahayujati TB, Damayanti SY, Tetteh KKA, Chitnis C, Stresman G, Cook J, Drakeley C. Using health facility-based serological surveillance to predict receptive areas at risk of malaria outbreaks in elimination areas. BMC Med 2020; 18:9. [PMID: 31987052 PMCID: PMC6986103 DOI: 10.1186/s12916-019-1482-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 12/09/2019] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND In order to improve malaria burden estimates in low transmission settings, more sensitive tools and efficient sampling strategies are required. This study evaluated the use of serological measures from repeated health facility-based cross-sectional surveys to investigate Plasmodium falciparum and Plasmodium vivax transmission dynamics in an area nearing elimination in Indonesia. METHODS Quarterly surveys were conducted in eight public health facilities in Kulon Progo District, Indonesia, from May 2017 to April 2018. Demographic data were collected from all clinic patients and their companions, with household coordinates collected using participatory mapping methods. In addition to standard microscopy tests, bead-based serological assays were performed on finger-prick bloodspot samples from 9453 people. Seroconversion rates (SCR, i.e. the proportion of people in the population who are expected to seroconvert per year) were estimated by fitting a simple reversible catalytic model to seroprevalence data. Mixed effects logistic regression was used to examine factors associated with malaria exposure, and spatial analysis was performed to identify areas with clustering of high antibody responses. RESULTS Parasite prevalence by microscopy was extremely low (0.06% (95% confidence interval 0.03-0.14, n = 6) and 0 for P. vivax and P. falciparum, respectively). However, spatial analysis of P. vivax antibody responses identified high-risk areas that were subsequently the site of a P. vivax outbreak in August 2017 (62 cases detected through passive and reactive detection systems). These areas overlapped with P. falciparum high-risk areas and were detected in each survey. General low transmission was confirmed by the SCR estimated from a pool of the four surveys in people aged 15 years old and under (0.020 (95% confidence interval 0.017-0.024) and 0.005 (95% confidence interval 0.003-0.008) for P. vivax and P. falciparum, respectively). The SCR estimates in those over 15 years old were 0.066 (95% confidence interval 0.041-0.105) and 0.032 (95% confidence interval 0.015-0.069) for P. vivax and P. falciparum, respectively. CONCLUSIONS These findings demonstrate the potential use of health facility-based serological surveillance to better identify and target areas still receptive to malaria in an elimination setting. Further implementation research is needed to enable integration of these methods with existing surveillance systems.
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Affiliation(s)
- Henry Surendra
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT UK
- Centre for Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jl. Medika, Yogyakarta, 55281 Indonesia
| | - Supargiyono
- Centre for Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jl. Medika, Yogyakarta, 55281 Indonesia
- Department of Parasitology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Sekip Utara, Yogyakarta, 55281 Indonesia
| | - Riris A. Ahmad
- Centre for Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jl. Medika, Yogyakarta, 55281 Indonesia
- Department of Biostatistics, Epidemiology and Population Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Sekip Utara, Yogyakarta, 55281 Indonesia
| | - Rizqiani A. Kusumasari
- Centre for Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jl. Medika, Yogyakarta, 55281 Indonesia
- Department of Parasitology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Sekip Utara, Yogyakarta, 55281 Indonesia
| | | | - Siska Y. Damayanti
- District Health Office of Kulon Progo, Jln. Suparman No 1, Wates, 55611 Indonesia
| | - Kevin K. A. Tetteh
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT UK
| | | | - Gillian Stresman
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT UK
| | - Jackie Cook
- MRC Tropical Epidemiology Group, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT UK
| | - Chris Drakeley
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT UK
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19
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Pringle JC, Tessema S, Wesolowski A, Chen A, Murphy M, Carpi G, Shields TM, Hamapumbu H, Searle KM, Kobayashi T, Katowa B, Musonda M, Stevenson JC, Thuma PE, Greenhouse B, Moss WJ, Norris DE. Genetic Evidence of Focal Plasmodium falciparum Transmission in a Pre-elimination Setting in Southern Province, Zambia. J Infect Dis 2020; 219:1254-1263. [PMID: 30445612 DOI: 10.1093/infdis/jiy640] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 11/09/2018] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Southern Province, Zambia has experienced a dramatic decline in Plasmodium falciparum malaria transmission in the past decade and is targeted for elimination. Zambia's National Malaria Elimination Program recommends reactive case detection (RCD) within 140 m of index households to enhance surveillance and eliminate remaining transmission foci. METHODS To evaluate whether RCD captures local transmission, we genotyped 26 microsatellites from 106 samples collected from index (n = 27) and secondary (n = 79) cases detected through RCD in the Macha Hospital catchment area between January 2015 and April 2016. RESULTS Participants from the same RCD event harbored more genetically related parasites than those from different RCD events, suggesting that RCD captures, at least in part, infections related through local transmission. Related parasites clustered in space and time, up to at least 250 m from index households. Spatial analysis identified a putative focal transmission hotspot. CONCLUSIONS The current RCD strategy detects focal transmission events, although programmatic guidelines to screen within 140 m of index households may fail to capture all secondary cases. This study highlights the utility of parasite genetic data in assessing programmatic interventions, and similar approaches may be useful to malaria elimination programs seeking to tailor intervention strategies to the underlying transmission epidemiology.
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Affiliation(s)
- Julia C Pringle
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Anna Chen
- Department of Medicine, University of California, San Francisco
| | - Maxwell Murphy
- Department of Medicine, University of California, San Francisco.,Division of Biostatistics, University of California, Berkeley
| | - Giovanna Carpi
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.,Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Timothy M Shields
- Department of Epidemiology, Johns Hopkins Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Kelly M Searle
- Department of Epidemiology, Johns Hopkins Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Tamaki Kobayashi
- Department of Epidemiology, Johns Hopkins Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | | | - Jennifer C Stevenson
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.,Macha Research Trust, Choma, Zambia
| | | | - Bryan Greenhouse
- Department of Medicine, University of California, San Francisco.,Chan Zuckerberg Biohub, San Francisco, California
| | - William J Moss
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.,Department of Epidemiology, Johns Hopkins Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Douglas E Norris
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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20
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Guerra CA, Citron DT, García GA, Smith DL. Characterising malaria connectivity using malaria indicator survey data. Malar J 2019; 18:440. [PMID: 31870353 PMCID: PMC6929427 DOI: 10.1186/s12936-019-3078-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Accepted: 12/14/2019] [Indexed: 12/13/2022] Open
Abstract
Malaria connectivity describes the flow of parasites among transmission sources and sinks within a given landscape. Because of the spatial and temporal scales at which parasites are transported by their hosts, malaria sub-populations are largely defined by mosquito movement and malaria connectivity among them is largely driven by human movement. Characterising malaria connectivity thus requires characterising human travel between areas with differing levels of exposure to malaria. Whilst understanding malaria connectivity is fundamental for optimising interventions, particularly in areas seeking or sustaining elimination, there is a dearth of human movement data required to achieve this goal. Malaria indicator surveys (MIS) are a generally under utilised but potentially rich source of travel data that provide a unique opportunity to study simple associations between malaria infection and human travel in large population samples. This paper shares the experience working with MIS data from Bioko Island that revealed programmatically useful information regarding malaria importation through human travel. Simple additions to MIS questionnaires greatly augmented the level of detail of the travel data, which can be used to characterise human travel patterns and malaria connectivity to assist targeting interventions. It is argued that MIS potentially represent very important and timely sources of travel data that need to be further exploited.
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Affiliation(s)
- Carlos A Guerra
- Medical Care Development International, 8401 Colesville Road, Suite 425, Silver Spring, MD, 20910, USA.
| | - Daniel T Citron
- Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Avenue, Seattle, 98121, USA
| | - Guillermo A García
- Medical Care Development International, 8401 Colesville Road, Suite 425, Silver Spring, MD, 20910, USA
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Avenue, Seattle, 98121, USA
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21
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Kagaya W, Gitaka J, Chan CW, Kongere J, Md Idris Z, Deng C, Kaneko A. Malaria resurgence after significant reduction by mass drug administration on Ngodhe Island, Kenya. Sci Rep 2019; 9:19060. [PMID: 31836757 PMCID: PMC6910941 DOI: 10.1038/s41598-019-55437-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 11/27/2019] [Indexed: 12/14/2022] Open
Abstract
Although WHO recommends mass drug administration (MDA) for malaria elimination, further evidence is required for understanding the obstacles for the optimum implementation of MDA. Just before the long rain in 2016, two rounds of MDA with artemisinin/piperaquine (Artequick) and low-dose primaquine were conducted with a 35-day interval for the entire population of Ngodhe Island (~500 inhabitants) in Lake Victoria, Kenya, which is surrounded by areas with moderate and high transmission. With approximately 90% compliance, Plasmodium prevalence decreased from 3% to 0% by microscopy and from 10% to 2% by PCR. However, prevalence rebounded to 9% by PCR two months after conclusion of MDA. Besides the remained local transmission, parasite importation caused by human movement likely contributed to the resurgence. Analyses of 419 arrivals to Ngodhe between July 2016 and September 2017 revealed Plasmodium prevalence of 4.6% and 16.0% by microscopy and PCR, respectively. Risk factors for infection among arrivals included age (0 to 5 and 11 to 15 years), and travelers from Siaya County, located to the north of Ngodhe Island. Parasite importation caused by human movement is one of major obstacles to sustain malaria elimination, suggesting the importance of cross-regional initiatives together with local vector control.
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Affiliation(s)
- Wataru Kagaya
- Department of Parasitology & Research Center for Infectious Disease Sciences, Graduate School of Medicine, Osaka City University, 1-4-3, Asahimachi, Abeno-ku, Osaka, 545-8585, Japan
| | - Jesse Gitaka
- Department of Clinical Medicine, Mount Kenya University, PO Box 342-01000, Thika, Kenya
| | - Chim W Chan
- Department of Parasitology & Research Center for Infectious Disease Sciences, Graduate School of Medicine, Osaka City University, 1-4-3, Asahimachi, Abeno-ku, Osaka, 545-8585, Japan.,Island Malaria Group, Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Biomedicum, Solnavägen 9, 171 65, Solna, Stockholm, Sweden.,Department of Anthropology, Binghamton University, Binghamton, NY, 13905, USA
| | - James Kongere
- Nairobi Research Station, Nagasaki University Institute of Tropical Medicine-Kenya Medical Research Institute (NUITM-KEMRI) Project, Institute of Tropical Medicine (NEKKEN), Nagasaki University, PO Box 19993-00202, Nairobi, Kenya
| | - Zulkarnain Md Idris
- Island Malaria Group, Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Biomedicum, Solnavägen 9, 171 65, Solna, Stockholm, Sweden.,Department of Parasitology and Medical Entomology, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, 56000, Kuala Lumpur, Malaysia
| | - Changsheng Deng
- Science and Technology Park, Guangzhou University of Chinese Medicine, Guangzhou, 510006, Guangdong, People's Republic of China
| | - Akira Kaneko
- Department of Parasitology & Research Center for Infectious Disease Sciences, Graduate School of Medicine, Osaka City University, 1-4-3, Asahimachi, Abeno-ku, Osaka, 545-8585, Japan. .,Island Malaria Group, Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Biomedicum, Solnavägen 9, 171 65, Solna, Stockholm, Sweden. .,Institute of Tropical Medicine (NEKKEN), Nagasaki University, Nagasaki, 1-12-4 Sakamoto, Nagasaki, 852-8523, Japan.
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22
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Malinga J, Mogeni P, Omedo I, Rockett K, Hubbart C, Jeffreys A, Williams TN, Kwiatkowski D, Bejon P, Ross A. Investigating the drivers of the spatio-temporal patterns of genetic differences between Plasmodium falciparum malaria infections in Kilifi County, Kenya. Sci Rep 2019; 9:19018. [PMID: 31836742 PMCID: PMC6911066 DOI: 10.1038/s41598-019-54348-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 11/12/2019] [Indexed: 01/17/2023] Open
Abstract
Knowledge of how malaria infections spread locally is important both for the design of targeted interventions aiming to interrupt malaria transmission and the design of trials to assess the interventions. A previous analysis of 1602 genotyped Plasmodium falciparum parasites in Kilifi, Kenya collected over 12 years found an interaction between time and geographic distance: the mean number of single nucleotide polymorphism (SNP) differences was lower for pairs of infections which were both a shorter time interval and shorter geographic distance apart. We determine whether the empiric pattern could be reproduced by a simple model, and what mean geographic distances between parent and offspring infections and hypotheses about genotype-specific immunity or a limit on the number of infections would be consistent with the data. We developed an individual-based stochastic simulation model of households, people and infections. We parameterized the model for the total number of infections, and population and household density observed in Kilifi. The acquisition of new infections, mutation, recombination, geographic location and clearance were included. We fit the model to the observed numbers of SNP differences between pairs of parasite genotypes. The patterns observed in the empiric data could be reproduced. Although we cannot rule out genotype-specific immunity or a limit on the number of infections per individual, they are not necessary to account for the observed patterns. The mean geographic distance between parent and offspring malaria infections for the base model was 0.5 km (95% CI 0.3-1.5), for a distribution with 68% of distances shorter than the mean. Very short mean distances did not fit well, but mixtures of distributions were also consistent with the data. For a pathogen which undergoes meiosis in a setting with moderate transmission and a low coverage of infections, analytic methods are limited but an individual-based model can be used with genotyping data to estimate parameter values and investigate hypotheses about underlying processes.
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Affiliation(s)
- Josephine Malinga
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Polycarp Mogeni
- Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Irene Omedo
- Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Kirk Rockett
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Christina Hubbart
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Anne Jeffreys
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Thomas N Williams
- Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
- Department of Medicine, South Kensington Campus, Imperial College London, London, UK
| | - Dominic Kwiatkowski
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Philip Bejon
- Kenya Medical Research Institute-Wellcome Trust Research Programme, Kilifi, Kenya
- Centre for Tropical Medicine & Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Amanda Ross
- Swiss Tropical and Public Health Institute, Basel, Switzerland.
- University of Basel, Basel, Switzerland.
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23
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Malinga J, Maia M, Moore S, Ross A. Can trials of spatial repellents be used to estimate mosquito movement? Parasit Vectors 2019; 12:421. [PMID: 31477155 PMCID: PMC6720076 DOI: 10.1186/s13071-019-3662-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Accepted: 08/09/2019] [Indexed: 11/16/2022] Open
Abstract
Background Knowledge of mosquito movement would aid the design of effective intervention strategies against malaria. However, data on mosquito movement through mark-recapture or genetics studies are challenging to collect, and so are not available for many sites. An additional source of information may come from secondary analyses of data from trials of repellents where household mosquito densities are collected. Using the study design of published trials, we developed a statistical model which can be used to estimate the movement between houses for mosquitoes displaced by a spatial repellent. The method uses information on the different distributions of mosquitoes between houses when no households are using spatial repellents compared to when there is incomplete coverage. The parameters to be estimated are the proportion of mosquitoes repelled, the proportion of those repelled that go to another house and the mean distance of movement between houses. Estimation is by maximum likelihood. Results We evaluated the method using simulation and found that data on the seasonal pattern of mosquito densities were required, which could be additionally collected during a trial. The method was able to provide accurate estimates from simulated data, except when the setting has few mosquitoes overall, few repelled, or the coverage with spatial repellent is low. The trial that motivated our analysis was found to have too few mosquitoes caught and repelled for our method to provide accurate results. Conclusions We propose that the method could be used as a secondary analysis of trial data to gain estimates of mosquito movement in the presence of repellents for trials with sufficient numbers of mosquitoes caught and repelled and with coverage levels which allow sufficient numbers of houses with and without repellent. Estimates from this method may supplement those from mark-release-recapture studies, and be used in designing effective malaria intervention strategies, parameterizing mathematical models and in designing trials of vector control interventions.
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Affiliation(s)
- Josephine Malinga
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Marta Maia
- KEMRI Wellcome Trust Research Programme, Kilifi, Kenya.,Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Sarah Moore
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland.,Ifakara Health Institute, Ifakara, Tanzania
| | - Amanda Ross
- Swiss Tropical and Public Health Institute, Basel, Switzerland. .,University of Basel, Basel, Switzerland.
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24
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Stresman G, Bousema T, Cook J. Malaria Hotspots: Is There Epidemiological Evidence for Fine-Scale Spatial Targeting of Interventions? Trends Parasitol 2019; 35:822-834. [PMID: 31474558 DOI: 10.1016/j.pt.2019.07.013] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 07/29/2019] [Accepted: 07/29/2019] [Indexed: 12/20/2022]
Abstract
As data at progressively granular spatial scales become available, the temptation is to target interventions to areas with higher malaria transmission - so-called hotspots - with the aim of reducing transmission in the wider community. This paper reviews literature to determine if hotspots are an intrinsic feature of malaria epidemiology and whether current evidence supports hotspot-targeted interventions. Hotspots are a consistent feature of malaria transmission at all endemicities. The smallest spatial unit capable of supporting transmission is the household, where peri-domestic transmission occurs. Whilst the value of focusing interventions to high-burden areas is evident, there is currently limited evidence that local-scale hotspots fuel transmission. As boundaries are often uncertain, there is no conclusive evidence that hotspot-targeted interventions accelerate malaria elimination.
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Affiliation(s)
- Gillian Stresman
- Infection Biology Department, London School of Hygiene and Tropical Medicine, London, UK.
| | - Teun Bousema
- Radboud University Medical Centre, Department of Microbiology, HB Nijmegen, The Netherlands.
| | - Jackie Cook
- Medical Research Council (MRC) Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK
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25
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Redmond SN, MacInnis BM, Bopp S, Bei AK, Ndiaye D, Hartl DL, Wirth DF, Volkman SK, Neafsey DE. De Novo Mutations Resolve Disease Transmission Pathways in Clonal Malaria. Mol Biol Evol 2019; 35:1678-1689. [PMID: 29722884 PMCID: PMC5995194 DOI: 10.1093/molbev/msy059] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Detecting de novo mutations in viral and bacterial pathogens enables researchers to reconstruct detailed networks of disease transmission and is a key technique in genomic epidemiology. However, these techniques have not yet been applied to the malaria parasite, Plasmodium falciparum, in which a larger genome, slower generation times, and a complex life cycle make them difficult to implement. Here, we demonstrate the viability of de novo mutation studies in P. falciparum for the first time. Using a combination of sequencing, library preparation, and genotyping methods that have been optimized for accuracy in low-complexity genomic regions, we have detected de novo mutations that distinguish nominally identical parasites from clonal lineages. Despite its slower evolutionary rate compared with bacterial or viral species, de novo mutation can be detected in P. falciparum across timescales of just 1–2 years and evolutionary rates in low-complexity regions of the genome can be up to twice that detected in the rest of the genome. The increased mutation rate allows the identification of separate clade expansions that cannot be found using previous genomic epidemiology approaches and could be a crucial tool for mapping residual transmission patterns in disease elimination campaigns and reintroduction scenarios.
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Affiliation(s)
- Seth N Redmond
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T.H. Chan School of Public Health, Boston, MA
| | - Bronwyn M MacInnis
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T.H. Chan School of Public Health, Boston, MA
| | - Selina Bopp
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T.H. Chan School of Public Health, Boston, MA
| | - Amy K Bei
- Harvard T.H. Chan School of Public Health, Boston, MA.,Department of Parasitology, Faculty of Medicine and Pharmacy, Cheikh Anta Diop University, Dakar, Senegal
| | - Daouda Ndiaye
- Harvard T.H. Chan School of Public Health, Boston, MA.,Department of Parasitology, Faculty of Medicine and Pharmacy, Cheikh Anta Diop University, Dakar, Senegal
| | - Daniel L Hartl
- Broad Institute of MIT and Harvard, Cambridge, MA.,Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA
| | - Dyann F Wirth
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T.H. Chan School of Public Health, Boston, MA
| | - Sarah K Volkman
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T.H. Chan School of Public Health, Boston, MA.,Department of Nursing, School of Nursing and Health Sciences, Simmons College, Boston, MA, 02115
| | - Daniel E Neafsey
- Broad Institute of MIT and Harvard, Cambridge, MA.,Harvard T.H. Chan School of Public Health, Boston, MA
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26
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Dalmat R, Naughton B, Kwan-Gett TS, Slyker J, Stuckey EM. Use cases for genetic epidemiology in malaria elimination. Malar J 2019; 18:163. [PMID: 31064369 PMCID: PMC6503548 DOI: 10.1186/s12936-019-2784-0] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 04/22/2019] [Indexed: 12/22/2022] Open
Abstract
Background While traditional epidemiological approaches have supported significant reductions in malaria incidence across many countries, higher resolution information about local and regional malaria epidemiology will be needed to efficiently target interventions for elimination. The application of genetic epidemiological methods for the analysis of parasite genetics has, thus far, primarily been confined to research settings. To illustrate how these technical methods can be used to advance programmatic and operational needs of National Malaria Control Programmes (NMCPs), and accelerate global progress to eradication, this manuscript presents seven use cases for which genetic epidemiology approaches to parasite genetic data are informative to the decision-making of NMCPs. Methods The use cases were developed through a highly iterative process that included an extensive review of the literature and global guidance documents, including the 2017 World Health Organization’s Framework for Malaria Elimination, and collection of stakeholder input. Semi-structured interviews were conducted with programmatic and technical experts about the needs and opportunities for genetic epidemiology methods in malaria elimination. Results Seven use cases were developed: Detect resistance, Assess drug resistance gene flow, Assess transmission intensity, Identify foci, Determine connectivity of parasite populations, Identify imported cases, and Characterize local transmission chains. The method currently used to provide the information sought, population unit for implementation, the pre-conditions for using these approaches, and post-conditions intended as a product of the use case were identified for each use case. Discussion This framework of use cases will prioritize research and development of genetic epidemiology methods that best achieve the goals of NMCPs, and ultimately, inform the establishment of normative policy guidance for their uses. With significant engagement of stakeholders from malaria endemic countries and collaboration with local programme experts to ensure strategic implementation, genetic epidemiological approaches have tremendous potential to accelerate global malaria elimination efforts. Electronic supplementary material The online version of this article (10.1186/s12936-019-2784-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ronit Dalmat
- Department of Epidemiology, University of Washington, Seattle, WA, USA.,Strategic Analysis Research and Training Center, University of Washington, Seattle, WA, USA
| | - Brienna Naughton
- Department of Global Health, University of Washington, Seattle, WA, USA.,Strategic Analysis Research and Training Center, University of Washington, Seattle, WA, USA
| | - Tao Sheng Kwan-Gett
- Department of Health Services, University of Washington, Seattle, WA, USA.,Strategic Analysis Research and Training Center, University of Washington, Seattle, WA, USA
| | - Jennifer Slyker
- Department of Epidemiology, University of Washington, Seattle, WA, USA.,Department of Global Health, University of Washington, Seattle, WA, USA.,Strategic Analysis Research and Training Center, University of Washington, Seattle, WA, USA
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27
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Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building. Epidemics 2019; 26:116-127. [PMID: 30446431 PMCID: PMC7105018 DOI: 10.1016/j.epidem.2018.10.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 08/06/2018] [Accepted: 10/17/2018] [Indexed: 12/24/2022] Open
Abstract
Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the 'big data' revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.
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28
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Greenhouse B, Daily J, Guinovart C, Goncalves B, Beeson J, Bell D, Chang MA, Cohen JM, Ding X, Domingo G, Eisele TP, Lammie PJ, Mayor A, Merienne N, Monteiro W, Painter J, Rodriguez I, White M, Drakeley C, Mueller I. Priority use cases for antibody-detecting assays of recent malaria exposure as tools to achieve and sustain malaria elimination. Gates Open Res 2019; 3:131. [PMID: 31172051 PMCID: PMC6545519 DOI: 10.12688/gatesopenres.12897.1] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/28/2019] [Indexed: 01/12/2023] Open
Abstract
Measurement of malaria specific antibody responses represents a practical and informative method for malaria control programs to assess recent exposure to infection. Technical advances in recombinant antigen production, serological screening platforms, and analytical methods have enabled the identification of several target antigens for laboratory based and point-of-contact tests. Questions remain as to how these serological assays can best be integrated into malaria surveillance activities to inform programmatic decision-making. This report synthesizes discussions from a convening at Institut Pasteur in Paris in June 2017 aimed at defining practical and informative use cases for serology applications and highlights five programmatic uses for serological assays including: documenting the absence of transmission; stratification of transmission; measuring the effect of interventions; informing a decentralized immediate response; and testing and treating P. vivax hypnozoite carriers.
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Affiliation(s)
- Bryan Greenhouse
- Department of Medicine,, University of California San Francisco, San Francisco, CA, USA
| | | | - Caterina Guinovart
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
- PATH, Seattle, WA, USA
| | | | | | - David Bell
- Intellectual Ventures, Bellevue, WA, USA
| | | | | | | | | | - Thomas P. Eisele
- Center for Applied Malaria Research and Evaluation, Tulane School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | | | - Alfredo Mayor
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
| | | | - Wuelto Monteiro
- Tropical Medicine Foundation Dr. Heitor Viera Dourado, Manaus, Amazonas, Brazil
| | - John Painter
- Centers of Disease Control and Prevention, Atlanta, GA, USA
| | - Isabel Rodriguez
- Department of Medicine,, University of California San Francisco, San Francisco, CA, USA
| | | | - Chris Drakeley
- London School of Tropical Medicine & Hygiene, London, UK
| | - Ivo Mueller
- Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
| | - The Malaria Serology Convening
- Department of Medicine,, University of California San Francisco, San Francisco, CA, USA
- Consultant to UNITAID, Denver, CO, USA
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
- PATH, Seattle, WA, USA
- London School of Tropical Medicine & Hygiene, London, UK
- The Burnet Institute, Melbourne, Australia
- Intellectual Ventures, Bellevue, WA, USA
- Centers of Disease Control and Prevention, Atlanta, GA, USA
- Clinton Health Access Initiative (CHAI), Boston, MA, USA
- FIND, Geneva, Switzerland
- Center for Applied Malaria Research and Evaluation, Tulane School of Public Health and Tropical Medicine, New Orleans, LA, USA
- Institut Pasteur, Paris, France
- Tropical Medicine Foundation Dr. Heitor Viera Dourado, Manaus, Amazonas, Brazil
- Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
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29
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Burkot TR, Bugoro H, Apairamo A, Cooper RD, Echeverry DF, Odabasi D, Beebe NW, Makuru V, Xiao H, Davidson JR, Deason NA, Reuben H, Kazura JW, Collins FH, Lobo NF, Russell TL. Spatial-temporal heterogeneity in malaria receptivity is best estimated by vector biting rates in areas nearing elimination. Parasit Vectors 2018; 11:606. [PMID: 30482239 PMCID: PMC6260740 DOI: 10.1186/s13071-018-3201-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 11/14/2018] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Decisions on when vector control can be withdrawn after malaria is eliminated depend on the receptivity or potential of an area to support vector populations. To guide malaria control and elimination programmes, the potential of biting rates, sporozoite rates, entomological inoculation rates and parity rates to estimate malaria receptivity and transmission were compared within and among geographically localised villages of active transmission in the Western Province of the Solomon Islands. RESULTS Malaria transmission and transmission potential was heterogeneous in both time and space both among and within villages as defined by anopheline species composition and biting densities. Biting rates during the peak biting period (from 18:00 to 00:00 h) of the primary vector, Anopheles farauti, ranged from less than 0.3 bites per person per half night in low receptivity villages to 26 bites per person in highly receptive villages. Within villages, sites with high anopheline biting rates were significantly clustered. Sporozoite rates provided evidence for continued transmission of Plasmodium falciparum, P. vivax and P. ovale by An. farauti and for incriminating An. hinesorum, as a minor vector, but were unreliable as indicators of transmission intensity. CONCLUSIONS In the low transmission area studied, sporozoite, entomological inoculation and parity rates could not be measured with the precision required to provide guidance to malaria programmes. Receptivity and potential transmission risk may be most reliably estimated by the vector biting rate. These results support the meaningful design of operational research programmes to ensure that resources are focused on providing information that can be utilised by malaria control programmes to best understand both transmission, transmission risk and receptivity across different areas.
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Affiliation(s)
- Thomas R. Burkot
- James Cook University, Australian Institute of Tropical Health and Medicine, Cairns, QLD 4870 Australia
| | - Hugo Bugoro
- National Vector Borne Disease Control Programme, Ministry of Health and Medical Services, Honiara, Solomon Islands
- Research Department, Solomon Islands National University, Honiara, Solomon Islands
| | - Allan Apairamo
- National Vector Borne Disease Control Programme, Ministry of Health and Medical Services, Honiara, Solomon Islands
| | - Robert D. Cooper
- Australian Army Malaria Institute, Gallipoli Barracks, Enoggera, 4052 Australia
| | - Diego F. Echeverry
- Department of Biological Sciences, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556 USA
| | - Danyal Odabasi
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Nigel W. Beebe
- University of Queensland, School of Biological Sciences, QLD, St. Lucia, 4068 Australia
- CSIRO, Dutton Park, Brisbane, QLD 4102 Australia
| | - Victoria Makuru
- Department of Biological Sciences, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556 USA
| | - Honglin Xiao
- Department of Biological Sciences, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556 USA
| | - Jenna R. Davidson
- Department of Biological Sciences, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556 USA
| | - Nicholas A. Deason
- Department of Biological Sciences, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556 USA
| | - Hedrick Reuben
- Western Province Malaria Control, Gizo, Western Province Solomon Islands
| | - James W. Kazura
- Center for Global Health & Diseases, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106–4983 USA
| | - Frank H. Collins
- Department of Biological Sciences, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556 USA
| | - Neil F. Lobo
- Department of Biological Sciences, Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556 USA
| | - Tanya L. Russell
- James Cook University, Australian Institute of Tropical Health and Medicine, Cairns, QLD 4870 Australia
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30
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Kang SY, Battle KE, Gibson HS, Cooper LV, Maxwell K, Kamya M, Lindsay SW, Dorsey G, Greenhouse B, Rodriguez-Barraquer I, Reiner RCJ, Smith DL, Bisanzio D. Heterogeneous exposure and hotspots for malaria vectors at three study sites in Uganda. Gates Open Res 2018; 2:32. [PMID: 30706054 PMCID: PMC6350504 DOI: 10.12688/gatesopenres.12838.2] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/01/2018] [Indexed: 11/30/2022] Open
Abstract
Background: Heterogeneity in malaria transmission has household, temporal, and spatial components. These factors are relevant for improving the efficiency of malaria control by targeting heterogeneity. To quantify variation, we analyzed mosquito counts from entomological surveillance conducted at three study sites in Uganda that varied in malaria transmission intensity. Mosquito biting or exposure is a risk factor for malaria transmission. Methods: Using a Bayesian zero-inflated negative binomial model, validated via a comprehensive simulation study, we quantified household differences in malaria vector density and examined its spatial distribution. We introduced a novel approach for identifying changes in vector abundance hotspots over time by computing the Getis-Ord statistic on ratios of household biting propensities for different scenarios. We also explored the association of household biting propensities with housing and environmental covariates. Results: In each site, there was evidence for hot and cold spots of vector abundance, and spatial patterns associated with urbanicity, elevation, or other environmental covariates. We found some differences in the hotspots in rainy vs. dry seasons or before vs. after the application of control interventions. Housing quality explained a portion of the variation among households in mosquito counts. Conclusion: This work provided an improved understanding of heterogeneity in malaria vector density at the three study sites in Uganda and offered a valuable opportunity for assessing whether interventions could be spatially targeted to be aimed at abundance hotspots which may increase malaria risk. Indoor residual spraying was shown to be a successful measure of vector control interventions in Tororo, Uganda. Cement walls, brick floors, closed eaves, screened airbricks, and tiled roofs were features of a house that had shown reduction of household biting propensity. Improvements in house quality should be recommended as a supplementary measure for malaria control reducing risk of infection.
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Affiliation(s)
- Su Yun Kang
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Katherine E Battle
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Harry S Gibson
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Laura V Cooper
- Department of Veterinary Medicine, Cambridge University, Cambridge, UK
| | - Kilama Maxwell
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Moses Kamya
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | | | - Grant Dorsey
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Bryan Greenhouse
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | | | - Robert C Jr Reiner
- Institute for Health Metrics & Evaluation, University of Washington, Seattle, WA, USA
| | - David L Smith
- Institute for Health Metrics & Evaluation, University of Washington, Seattle, WA, USA
| | - Donal Bisanzio
- RTI International, Washington DC, USA.,Centre for Tropical Diseases, Sacro Cuore-Don Calabria Hospital, Negrar, Italy
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31
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Kraemer MUG, Cummings DAT, Funk S, Reiner RC, Faria NR, Pybus OG, Cauchemez S. Reconstruction and prediction of viral disease epidemics. Epidemiol Infect 2018; 147:e34. [PMID: 30394230 PMCID: PMC6398585 DOI: 10.1017/s0950268818002881] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 08/24/2018] [Accepted: 09/21/2018] [Indexed: 01/29/2023] Open
Abstract
A growing number of infectious pathogens are spreading among geographic regions. Some pathogens that were previously not considered to pose a general threat to human health have emerged at regional and global scales, such as Zika and Ebola Virus Disease. Other pathogens, such as yellow fever virus, were previously thought to be under control but have recently re-emerged, causing new challenges to public health organisations. A wide array of new modelling techniques, aided by increased computing capabilities, novel diagnostic tools, and the increased speed and availability of genomic sequencing allow researchers to identify new pathogens more rapidly, assess the likelihood of geographic spread, and quantify the speed of human-to-human transmission. Despite some initial successes in predicting the spread of acute viral infections, the practicalities and sustainability of such approaches will need to be evaluated in the context of public health responses.
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Affiliation(s)
- M. U. G. Kraemer
- Harvard Medical School, Harvard University, Boston, MA, USA
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA, USA
- Department of Zoology, University of Oxford, Oxford, UK
| | - D. A. T. Cummings
- Department of Biology, University of Florida, Gainesville, Florida, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - S. Funk
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - R. C. Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA
| | - N. R. Faria
- Department of Zoology, University of Oxford, Oxford, UK
| | - O. G. Pybus
- Department of Zoology, University of Oxford, Oxford, UK
| | - S. Cauchemez
- Mathematical Modelling of Infectious Diseases and Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, France
- CNRS UMR2000: Génomique évolutive, modélisation et santé, Paris, France
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32
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Ihantamalala FA, Herbreteau V, Rakotoarimanana FMJ, Rakotondramanga JM, Cauchemez S, Rahoilijaona B, Pennober G, Buckee CO, Rogier C, Metcalf CJE, Wesolowski A. Estimating sources and sinks of malaria parasites in Madagascar. Nat Commun 2018; 9:3897. [PMID: 30254280 PMCID: PMC6156502 DOI: 10.1038/s41467-018-06290-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Accepted: 08/21/2018] [Indexed: 02/03/2023] Open
Abstract
In areas where malaria epidemiology is spatially and temporally heterogeneous, human-mediated parasite importation can result in non-locally acquired clinical cases and outbreaks in low-transmission areas. Using mobility estimates derived from the mobile phone data and spatial malaria prevalence data, we identify travel routes relevant to malaria transmission in Madagascar. We find that the primary hubs of parasite importation are in a spatially connected area of the central highlands. Surprisingly, sources of these imported infections are not spatially clustered. We then related these source locations directly to clinical cases in the low-transmission area of the capital. We find that in the capital, a major sink, the primary sources of infection are along the more populated coastal areas, although these sources are seasonally variable. Our results have implications for targeting interventions at source locations to achieve local or national malaria control goals.
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Affiliation(s)
- Felana Angella Ihantamalala
- Institut Pasteur de Madagascar, 101 Antanarivo, Madagascar.,UMR 228 ESPACE-DEV (IRD, UM2, UR, UAG), Station SEAS-Ol, Saint-Pierre, Reunion, France
| | - Vincent Herbreteau
- UMR 228 ESPACE-DEV (IRD, UM2, UR, UAG), Station SEAS-Ol, Saint-Pierre, Reunion, France
| | | | | | - Simon Cauchemez
- Mathematical Modeling of Infectious Diseases Unit, Institut Pasteur, Paris, 75015, France.,Centre National de la Recherche Scientifique, URA3012, Paris, 75015, France.,Center of Bioinformatics, Biostatistics and Integrative Biology, Institut Pasteur, Paris, 75015, France
| | | | - Gwenaëlle Pennober
- UMR 228 ESPACE-DEV (IRD, UM2, UR, UAG), Station SEAS-Ol, Saint-Pierre, Reunion, France
| | - Caroline O Buckee
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, 02115, USA.,Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, MA, 02115, USA
| | - Christophe Rogier
- Unité de recherche sur les maladies infectieuses et tropicales émergentes (URMITE), Paris, France.,Institute of Biomedical Research of the French Armed Forces (IRBA), Brétigny-Sur-Orge, France
| | - C J E Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, 08540, USA.,Woodrow Wilson School of Public Affairs, Princeton University, Princeton, NJ, 08540, USA
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21231, USA.
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33
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Stresman GH, Mwesigwa J, Achan J, Giorgi E, Worwui A, Jawara M, Di Tanna GL, Bousema T, Van Geertruyden JP, Drakeley C, D'Alessandro U. Do hotspots fuel malaria transmission: a village-scale spatio-temporal analysis of a 2-year cohort study in The Gambia. BMC Med 2018; 16:160. [PMID: 30213275 PMCID: PMC6137946 DOI: 10.1186/s12916-018-1141-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Accepted: 07/31/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Despite the biological plausibility of hotspots fueling malaria transmission, the evidence to support this concept has been mixed. If transmission spreads from high burden to low burden households in a consistent manner, then this could have important implications for control and elimination program development. METHODS Data from a longitudinal cohort in The Gambia was analyzed. All consenting individuals residing in 12 villages across the country were sampled monthly from June (dry season) to December 2013 (wet season), in April 2014 (mid dry season), and monthly from June to December 2014. A study nurse stationed within each village recorded passively detected malaria episodes between visits. Plasmodium falciparum infections were determined by polymerase chain reaction and analyzed using a geostatistical model. RESULTS Household-level observed monthly incidence ranged from 0 to 0.50 infection per person (interquartile range = 0.02-0.10) across the sampling months, and high burden households exist across all study villages. There was limited evidence of a spatio-temporal pattern at the monthly timescale irrespective of transmission intensity. Within-household transmission was the most plausible hypothesis examined to explain the observed heterogeneity in infections. CONCLUSIONS Within-village malaria transmission patterns are concentrated in a small proportion of high burden households, but patterns are stochastic regardless of endemicity. Our findings support the notion of transmission occurring at the household and village scales but not the use of a targeted approach to interrupt spreading of infections from high to low burden areas within villages in this setting.
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Affiliation(s)
- Gillian H Stresman
- Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, UK.
| | - Julia Mwesigwa
- Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine, Fajara, The Gambia.,University of Antwerp, Antwerp, Belgium
| | - Jane Achan
- Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine, Fajara, The Gambia.,University of Antwerp, Antwerp, Belgium
| | - Emanuele Giorgi
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, UK
| | - Archibald Worwui
- Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine, Fajara, The Gambia.,University of Antwerp, Antwerp, Belgium
| | - Musa Jawara
- Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine, Fajara, The Gambia.,University of Antwerp, Antwerp, Belgium
| | | | - Teun Bousema
- Department of Medical Microbology, Radboud Medical University, Nijmegen, The Netherlands
| | | | - Chris Drakeley
- Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, UK
| | - Umberto D'Alessandro
- Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, UK.,Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine, Fajara, The Gambia.,University of Antwerp, Antwerp, Belgium
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34
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Hellewell J, Walker P, Ghani A, Rao B, Churcher TS. Using ante-natal clinic prevalence data to monitor temporal changes in malaria incidence in a humanitarian setting in the Democratic Republic of Congo. Malar J 2018; 17:312. [PMID: 30157850 PMCID: PMC6114784 DOI: 10.1186/s12936-018-2460-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 08/17/2018] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND The number of clinical cases of malaria is often recorded in resource constrained or conflict settings as a proxy for disease burden. Interpreting case count data in areas of humanitarian need is challenging due to uncertainties in population size caused by security concerns, resource constraints and population movement. Malaria prevalence in women visiting ante-natal care (ANC) clinics has the potential to be an easier and more accurate metric for malaria surveillance that is unbiased by population size if malaria testing is routinely conducted irrespective of symptoms. METHODS A suite of distributed lag non-linear models was fitted to clinical incidence time-series data in children under 5 years and ANC prevalence data from health centres run by Médecins Sans Frontières in the Democratic Republic of Congo, which implement routine intermittent screening and treatment alongside intermittent preventative treatment in pregnancy. These statistical models enable the temporal relationship between the two metrics to be disentangled. RESULTS There was a strong relationship between the ANC prevalence and clinical incidence suggesting that both can be used to describe current malaria endemicity. There was no evidence that ANC prevalence could predict future clinical incidence, though a change in clinical incidence was shown to influence ANC prevalence up to 3 months into the future. CONCLUSIONS The results indicate that ANC prevalence may be a suitable metric for retrospective evaluations of the impact of malaria interventions and is a useful method for evaluating long-term malaria trends in resource constrained settings.
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Affiliation(s)
- Joel Hellewell
- MRC Centre for Outbreak Analysis and Modelling, Imperial College London, London, UK.
| | - Patrick Walker
- MRC Centre for Outbreak Analysis and Modelling, Imperial College London, London, UK
| | - Azra Ghani
- MRC Centre for Outbreak Analysis and Modelling, Imperial College London, London, UK
| | - Bhargavi Rao
- Manson Unit, Médecins Sans Frontières (Operational Centre Amsterdam), London, UK
| | - Thomas S Churcher
- MRC Centre for Outbreak Analysis and Modelling, Imperial College London, London, UK
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35
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Kang SY, Battle KE, Gibson HS, Cooper LV, Maxwell K, Kamya M, Lindsay SW, Dorsey G, Greenhouse B, Rodriguez-Barraquer I, Reiner RCJ, Smith DL, Bisanzio D. Heterogeneous exposure and hotspots for malaria vectors at three study sites in Uganda. Gates Open Res 2018. [DOI: 10.12688/gatesopenres.12838.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background: Heterogeneity in malaria transmission has household, temporal, and spatial components. These factors are relevant for improving the efficiency of malaria control by targeting heterogeneity. To quantify variation, we analyzed mosquito counts from entomological surveillance conducted at three study sites in Uganda that varied in malaria transmission intensity. Methods: Using a Bayesian zero-inflated negative binomial model, validated via a comprehensive simulation study, we quantified household differences in malaria vector density and examined its spatial distribution. We introduced a novel approach for identifying changes in malaria hotspots over time by computing the Getis-Ord statistic on ratios of household biting propensities for different scenarios. We also explored the association of household biting propensities with housing and environmental covariates. Results: In each site, there was evidence for hot and cold spots, spatial patterns associated with urbanicity, elevation, or other environmental covariates. We found some differences in the hotspots in rainy vs. dry seasons or before vs. after the application of control interventions. Housing quality explained a portion of the variation among households in mosquito counts. Conclusion: This work provided an improved understanding of heterogeneity in malaria vector density at the three study sites in Uganda and offered a valuable opportunity for assessing whether interventions could be spatially targeted to be aimed at hotspots of malaria risk. Indoor residual spraying was shown to be a successful measure of vector control interventions in Tororo, Uganda. Cement walls, brick floors, closed eaves, screened airbricks, and tiled roofs were features of a house that had shown protective effects towards malaria risk. Improvements in house quality should be recommended as a supplementary measure for malaria control.
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36
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Routledge I, Chevéz JER, Cucunubá ZM, Rodriguez MG, Guinovart C, Gustafson KB, Schneider K, Walker PGT, Ghani AC, Bhatt S. Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting. Nat Commun 2018; 9:2476. [PMID: 29946060 PMCID: PMC6018772 DOI: 10.1038/s41467-018-04577-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 05/02/2018] [Indexed: 01/08/2023] Open
Abstract
In 2016 the World Health Organization identified 21 countries that could eliminate malaria by 2020. Monitoring progress towards this goal requires tracking ongoing transmission. Here we develop methods that estimate individual reproduction numbers and their variation through time and space. Individual reproduction numbers, Rc, describe the state of transmission at a point in time and differ from mean reproduction numbers, which are averages of the number of people infected by a typical case. We assess elimination progress in El Salvador using data for confirmed cases of malaria from 2010 to 2016. Our results demonstrate that whilst the average number of secondary malaria cases was below one (0.61, 95% CI 0.55–0.65), individual reproduction numbers often exceeded one. We estimate a decline in Rc between 2010 and 2016. However we also show that if importation is maintained at the same rate, the country may not achieve malaria elimination by 2020. Twenty one countries have been identified for malaria elimination by 2020 and their progress needs to be constantly evaluated. Here, the authors present a method that estimates individual reproduction numbers and their variation through time and space and use it to monitor elimination success in El Salvador between 2010 and 2016.
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Affiliation(s)
- Isobel Routledge
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK.
| | | | - Zulma M Cucunubá
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK
| | | | | | | | | | - Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK
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37
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Cohen JM, Le Menach A, Pothin E, Eisele TP, Gething PW, Eckhoff PA, Moonen B, Schapira A, Smith DL. Mapping multiple components of malaria risk for improved targeting of elimination interventions. Malar J 2017; 16:459. [PMID: 29132357 PMCID: PMC5683539 DOI: 10.1186/s12936-017-2106-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 11/02/2017] [Indexed: 11/13/2022] Open
Abstract
There is a long history of considering the constituent components of malaria risk and the malaria transmission cycle via the use of mathematical models, yet strategic planning in endemic countries tends not to take full advantage of available disease intelligence to tailor interventions. National malaria programmes typically make operational decisions about where to implement vector control and surveillance activities based upon simple categorizations of annual parasite incidence. With technological advances, an enormous opportunity exists to better target specific malaria interventions to the places where they will have greatest impact by mapping and evaluating metrics related to a variety of risk components, each of which describes a different facet of the transmission cycle. Here, these components and their implications for operational decision-making are reviewed. For each component, related mappable malaria metrics are also described which may be measured and evaluated by malaria programmes seeking to better understand the determinants of malaria risk. Implementing tailored programmes based on knowledge of the heterogeneous distribution of the drivers of malaria transmission rather than only consideration of traditional metrics such as case incidence has the potential to result in substantial improvements in decision-making. As programmes improve their ability to prioritize their available tools to the places where evidence suggests they will be most effective, elimination aspirations may become increasingly feasible.
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Affiliation(s)
- Justin M Cohen
- Clinton Health Access Initiative, 383 Dorchester Ave., Suite 400, Boston, MA, 02127, USA.
| | - Arnaud Le Menach
- Clinton Health Access Initiative, 383 Dorchester Ave., Suite 400, Boston, MA, 02127, USA
| | - Emilie Pothin
- Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051, Basel, Switzerland
| | - Thomas P Eisele
- Center for Applied Malaria Research and Evaluation, Tulane University School of Public Health and Tropical Medicine, 1440 Canal St (2300), New Orleans, LA, 70112, USA
| | - Peter W Gething
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7LF, UK
| | - Philip A Eckhoff
- Institute for Disease Modeling, Building IV, 3150 139th Ave SE, Bellevue, WA, 98005, USA
| | - Bruno Moonen
- Bill & Melinda Gates Foundation, PO Box 23350, Seattle, WA, 98102, USA
| | | | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA
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38
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Abstract
This paper summarises key advances and priorities since the 2011 presentation of the Malaria Eradication Research Agenda (malERA), with a focus on the combinations of intervention tools and strategies for elimination and their evaluation using modelling approaches. With an increasing number of countries embarking on malaria elimination programmes, national and local decisions to select combinations of tools and deployment strategies directed at malaria elimination must address rapidly changing transmission patterns across diverse geographic areas. However, not all of these approaches can be systematically evaluated in the field. Thus, there is potential for modelling to investigate appropriate 'packages' of combined interventions that include various forms of vector control, case management, surveillance, and population-based approaches for different settings, particularly at lower transmission levels. Modelling can help prioritise which intervention packages should be tested in field studies, suggest which intervention package should be used at a particular level or stratum of transmission intensity, estimate the risk of resurgence when scaling down specific interventions after local transmission is interrupted, and evaluate the risk and impact of parasite drug resistance and vector insecticide resistance. However, modelling intervention package deployment against a heterogeneous transmission background is a challenge. Further validation of malaria models should be pursued through an iterative process, whereby field data collected with the deployment of intervention packages is used to refine models and make them progressively more relevant for assessing and predicting elimination outcomes.
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39
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Yukich JO, Chitnis N. Modelling the implications of stopping vector control for malaria control and elimination. Malar J 2017; 16:411. [PMID: 29029609 PMCID: PMC5640964 DOI: 10.1186/s12936-017-2051-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Accepted: 10/04/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Increasing coverage of malaria vector control interventions globally has led to significant reductions in disease burden. However due to its high recurrent cost, there is a need to determine if and when vector control can be safely scaled back after transmission has been reduced. METHODS AND FINDINGS A mathematical model of Plasmodium falciparum malaria epidemiology was simulated to determine the impact of scaling back vector control on transmission and disease. A regression analysis of simulation results was conducted to derive predicted probabilities of resurgence, severity of resurgence and time to resurgence under various settings. Results indicate that, in the absence of secular changes in transmission, there are few scenarios where vector control can be removed without high expectation of resurgence. These, potentially safe, scenarios are characterized by low historic entomological inoculation rates, successful vector control programmes that achieve elimination or near elimination, and effective surveillance systems with high coverage and effective treatment of malaria cases. CONCLUSIONS Programmes and funding agencies considering scaling back or withdrawing vector control from previously malaria endemic areas need to first carefully consider current receptivity and other available interventions in a risk assessment. Surveillance for resurgence needs to be continuously conducted over a long period of time in order to ensure a rapid response should vector control be withdrawn.
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Affiliation(s)
- Joshua O. Yukich
- Center for Applied Malaria Research and Evaluation, Tulane University School of Public Health and Tropical Medicine, 1440 Canal St. 8317, New Orleans, LA 70112 USA
| | - Nakul Chitnis
- Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland
- University of Basel, Basel, Switzerland
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40
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Tejedor-Garavito N, Dlamini N, Pindolia D, Soble A, Ruktanonchai NW, Alegana V, Le Menach A, Ntshalintshali N, Dlamini B, Smith DL, Tatem AJ, Kunene S. Travel patterns and demographic characteristics of malaria cases in Swaziland, 2010-2014. Malar J 2017; 16:359. [PMID: 28886710 PMCID: PMC5591561 DOI: 10.1186/s12936-017-2004-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Accepted: 08/30/2017] [Indexed: 11/17/2022] Open
Abstract
Background As 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. Methods Travel 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. Results Of 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. Conclusion Results 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. Electronic supplementary material The online version of this article (doi:10.1186/s12936-017-2004-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | | | | | - Adam Soble
- Clinton Health Access Initiative, Boston, MA, USA
| | - Nick W Ruktanonchai
- WorldPop, University of Southampton, Southampton, UK.,Flowminder Foundation, Stockholm, Sweden
| | - Victor Alegana
- WorldPop, University of Southampton, Southampton, UK.,Flowminder Foundation, Stockholm, Sweden
| | | | | | | | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA
| | - Andrew J Tatem
- WorldPop, University of Southampton, Southampton, UK.,Flowminder Foundation, Stockholm, Sweden
| | - Simon Kunene
- National Malaria Control Programme, Manzini, Swaziland
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41
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Siraj AS, Oidtman RJ, Huber JH, Kraemer MUG, Brady OJ, Johansson MA, Perkins TA. Temperature modulates dengue virus epidemic growth rates through its effects on reproduction numbers and generation intervals. PLoS Negl Trop Dis 2017; 11:e0005797. [PMID: 28723920 PMCID: PMC5536440 DOI: 10.1371/journal.pntd.0005797] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Revised: 07/31/2017] [Accepted: 07/11/2017] [Indexed: 12/16/2022] Open
Abstract
Epidemic growth rate, r, provides a more complete description of the potential for epidemics than the more commonly studied basic reproduction number, R0, yet the former has never been described as a function of temperature for dengue virus or other pathogens with temperature-sensitive transmission. The need to understand the drivers of epidemics of these pathogens is acute, with arthropod-borne virus epidemics becoming increasingly problematic. We addressed this need by developing temperature-dependent descriptions of the two components of r—R0 and the generation interval—to obtain a temperature-dependent description of r. Our results show that the generation interval is highly sensitive to temperature, decreasing twofold between 25 and 35°C and suggesting that dengue virus epidemics may accelerate as temperatures increase, not only because of more infections per generation but also because of faster generations. Under the empirical temperature relationships that we considered, we found that r peaked at a temperature threshold that was robust to uncertainty in model parameters that do not depend on temperature. Although the precise value of this temperature threshold could be refined following future studies of empirical temperature relationships, the framework we present for identifying such temperature thresholds offers a new way to classify regions in which dengue virus epidemic intensity could either increase or decrease under future climate change. Recurrent, rapidly intensifying epidemics of dengue–the world’s most prevalent mosquito-borne viral disease–pose a challenge to healthcare systems throughout the tropical and subtropical world. An acute disease that tends to respond well to proper treatment, the sometimes intense nature of dengue epidemics has been known to overwhelm healthcare systems and elevate the morbidity and mortality of patients left without adequate medical treatment under peak epidemic conditions. Here, we quantify the temperature dependence of dengue epidemic intensity by quantifying two distinct determinants of epidemic growth rate: the average number of secondary infections arising from each primary infection and the average time between successive infections in humans. Our results show that the time between successive infections in humans decreases steadily with increasing temperatures, whereas the average number of secondary infections peaks at intermediate temperatures. Altogether, this suggests a peak temperature for dengue epidemic intensity. Applying this result to global temperature projections under future climate change scenarios suggests that dengue epidemics in many regions of the world could become more intense under future temperature increases.
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Affiliation(s)
- Amir S. Siraj
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, United States of America
- * E-mail: (ASS); (TAP)
| | - Rachel J. Oidtman
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, United States of America
| | - John H. Huber
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, United States of America
| | - Moritz U. G. Kraemer
- Department of Zoology, University of Oxford, Oxford, United Kingdom
- Department of Pediatrics, Harvard Medical School, Boston, United States of America
- Department of Informatics, Boston Children’s Hospital, Boston, United States of America
| | - Oliver J. Brady
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Michael A. Johansson
- Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
- Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, United States of America
| | - T. Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, United States of America
- * E-mail: (ASS); (TAP)
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Chuang TW, Soble A, Ntshalintshali N, Mkhonta N, Seyama E, Mthethwa S, Pindolia D, Kunene S. Assessment of climate-driven variations in malaria incidence in Swaziland: toward malaria elimination. Malar J 2017; 16:232. [PMID: 28571572 PMCID: PMC5455096 DOI: 10.1186/s12936-017-1874-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Accepted: 05/24/2017] [Indexed: 12/01/2022] Open
Abstract
Background Swaziland aims to eliminate malaria by 2020. However, imported cases from neighbouring endemic countries continue to sustain local parasite reservoirs and initiate transmission. As certain weather and climatic conditions may trigger or intensify malaria outbreaks, identification of areas prone to these conditions may aid decision-makers in deploying targeted malaria interventions more effectively. Methods Malaria case-surveillance data for Swaziland were provided by Swaziland’s National Malaria Control Programme. Climate data were derived from local weather stations and remote sensing images. Climate parameters and malaria cases between 2001 and 2015 were then analysed using seasonal autoregressive integrated moving average models and distributed lag non-linear models (DLNM). Results The incidence of malaria in Swaziland increased between 2005 and 2010, especially in the Lubombo and Hhohho regions. A time-series analysis indicated that warmer temperatures and higher precipitation in the Lubombo and Hhohho administrative regions are conducive to malaria transmission. DLNM showed that the risk of malaria increased in Lubombo when the maximum temperature was above 30 °C or monthly precipitation was above 5 in. In Hhohho, the minimum temperature remaining above 15 °C or precipitation being greater than 10 in. might be associated with malaria transmission. Conclusions This study provides a preliminary assessment of the impact of short-term climate variations on malaria transmission in Swaziland. The geographic separation of imported and locally acquired malaria, as well as population behaviour, highlight the varying modes of transmission, part of which may be relevant to climate conditions. Thus, the impact of changing climate conditions should be noted as Swaziland moves toward malaria elimination. Electronic supplementary material The online version of this article (doi:10.1186/s12936-017-1874-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ting-Wu Chuang
- Department of Molecular Parasitology and Tropical Diseases, School of Medicine, College of Medicine, Taipei Medical University, No. 250, Wuxing St. Sinyi District, Taipei, 100, Taiwan.
| | - Adam Soble
- Clinton Health Access Initiative, Manzini, Swaziland
| | | | - Nomcebo Mkhonta
- National Malaria Control Programme, Ministry of Health, Manzini, Swaziland
| | - Eric Seyama
- Swaziland Meteorological Service, Mbabane, Swaziland
| | - Steven Mthethwa
- National Malaria Control Programme, Ministry of Health, Manzini, Swaziland
| | | | - Simon Kunene
- National Malaria Control Programme, Ministry of Health, Manzini, Swaziland
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Quantitative, model-based estimates of variability in the generation and serial intervals of Plasmodium falciparum malaria. Malar J 2016; 15:490. [PMID: 27660051 PMCID: PMC5034682 DOI: 10.1186/s12936-016-1537-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 09/14/2016] [Indexed: 11/10/2022] Open
Abstract
Background The serial interval is a fundamentally important quantity in infectious disease epidemiology that has numerous applications to inferring patterns of transmission from case data. Many of these applications are apropos of efforts to eliminate falciparum malaria from locations throughout the world, yet the serial interval for this disease is poorly understood quantitatively. Methods To obtain a quantitative estimate of the serial interval for falciparum malaria, the sum of the components of the falciparum malaria transmission cycle was taken based on a combination of mathematical models and empirical data. During this process, a number of factors were identified that account for substantial variability in the serial interval across different contexts. Results Treatment with anti-malarial drugs roughly halves the serial interval due to an abbreviated period of human infectiousness, seasonality results in different serial intervals at different points in the transmission season, and variability in within-host dynamics results in many individuals whose serial intervals do not follow average behaviour. Furthermore, 24.5 % of secondary cases presenting clinically did so prior to the primary cases being identified through active detection of infection. Conclusions These results have important implications for epidemiological applications that rely on quantitative estimates of the serial interval of falciparum malaria and other diseases characterized by prolonged infections and complex ecological drivers.
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Mapping Malaria Risk in Low Transmission Settings: Challenges and Opportunities. Trends Parasitol 2016; 32:635-645. [PMID: 27238200 DOI: 10.1016/j.pt.2016.05.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2016] [Revised: 04/29/2016] [Accepted: 05/02/2016] [Indexed: 11/24/2022]
Abstract
As malaria transmission declines, it becomes increasingly focal and prone to outbreaks. Understanding and predicting patterns of transmission risk becomes an important component of an effective elimination campaign, allowing limited resources for control and elimination to be targeted cost-effectively. Malaria risk mapping in low transmission settings is associated with some unique challenges. This article reviews the main challenges and opportunities related to risk mapping in low transmission areas including recent advancements in risk mapping low transmission malaria, relevant metrics, and statistical approaches and risk mapping in post-elimination settings.
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Advances in mapping malaria for elimination: fine resolution modelling of Plasmodium falciparum incidence. Sci Rep 2016; 6:29628. [PMID: 27405532 PMCID: PMC4942778 DOI: 10.1038/srep29628] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Accepted: 06/22/2016] [Indexed: 10/31/2022] Open
Abstract
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.
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Wahid S, Stresman GH, Kamal SS, Sepulveda N, Kleinschmidt I, Bousema T, Drakeley C. Heterogeneous malaria transmission in long-term Afghan refugee populations: a cross-sectional study in five refugee camps in northern Pakistan. Malar J 2016; 15:245. [PMID: 27121196 PMCID: PMC4847188 DOI: 10.1186/s12936-016-1305-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Accepted: 04/20/2016] [Indexed: 11/10/2022] Open
Abstract
Background Afghan refugees in northern Pakistan have been resident for over 30 years and current information on malaria in this population is sparse. Understanding malaria risk and distribution in refugee camps is important for effective management both in camps and on return to Afghanistan. Methods Cross-sectional malariometric surveys were conducted in five Afghan refugee camps to determine infection and exposure to both Plasmodium falciparum and Plasmodium vivax. Factors associated with malaria infection and exposure were analysed using logistic regression, and spatial heterogeneity within camps was investigated with SatScan. Results In this low-transmission setting, prevalence of infection in the five camps ranged from 0–0.2 to 0.4–9 % by rapid diagnostic test and 0–1.39 and 5–15 % by polymerase chain reaction for P. falciparum and P. vivax, respectively. Prevalence of anti-malarial antibodies to P. falciparum antigens was 3–11 and 17–45 % for P. vivax antigens. Significant foci of P. vivax infection and exposure were detected in three of the five camps. Hotspots of P. falciparum were also detected in three camps, only one of which also showed evidence of P. vivax hotspots. Conclusions There is low and spatially heterogeneous malaria transmission in the refugee camps in northern Pakistan. Understanding malaria risk in refugee camps is important so the malaria risk faced by these populations in the camps and upon their return to Afghanistan can be effectively managed.
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Affiliation(s)
- Sobia Wahid
- Department of Zoology, University of Peshawar, Peshawar, Pakistan
| | - Gillian H Stresman
- Department of Immunology and Infection, London School of Hygiene & Tropical Medicine, Keppel Street, London, WCIE 7HT, UK
| | - Syed Sajid Kamal
- Vector Control Department, Merlin Malaria Control Programme, Khyber Pakhtunkhwa, Pakistan
| | - Nuno Sepulveda
- Department of Immunology and Infection, London School of Hygiene & Tropical Medicine, Keppel Street, London, WCIE 7HT, UK
| | - Immo Kleinschmidt
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London, WCIE 7HT, UK
| | - Teun Bousema
- Department of Immunology and Infection, London School of Hygiene & Tropical Medicine, Keppel Street, London, WCIE 7HT, UK.,Department of Medical Microbiology, Radboud University Medical Center, 6500, Nijmegen, The Netherlands
| | - Chris Drakeley
- Department of Immunology and Infection, London School of Hygiene & Tropical Medicine, Keppel Street, London, WCIE 7HT, UK.
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