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Maiti M, Roy U. Space-time clusters and co-occurrence of Plasmodium vivax and Plasmodium falciparum malaria in West Bengal, India. Malar J 2024; 23:189. [PMID: 38880891 PMCID: PMC11181534 DOI: 10.1186/s12936-024-05015-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 06/10/2024] [Indexed: 06/18/2024] Open
Abstract
BACKGROUND Malaria, a prominent vector borne disease causing over a million annual cases worldwide, predominantly affects vulnerable populations in the least developed regions. Despite their preventable and treatable nature, malaria remains a global public health concern. In the last decade, India has faced a significant decline in malaria morbidity and mortality. As India pledged to eliminate malaria by 2030, this study examined a decade of surveillance data to uncover space-time clustering and seasonal trends of Plasmodium vivax and Plasmodium falciparum malaria cases in West Bengal. METHODS Seasonal and trend decomposition using Loess (STL) was applied to detect seasonal trend and anomaly of the time series. Univariate and multivariate space-time cluster analysis of both malaria cases were performed at block level using Kulldorff's space-time scan statistics from April 2011 to March 2021 to detect statistically significant space-time clusters. RESULTS From the time series decomposition, a clear seasonal pattern is visible for both malaria cases. Statistical analysis indicated considerable high-risk P. vivax clusters, particularly in the northern, central, and lower Gangetic areas. Whereas, P. falciparum was concentrated in the western region with a significant recent transmission towards the lower Gangetic plain. From the multivariate space-time scan statistics, the co-occurrence of both cases were detected with four significant clusters, which signifies the regions experiencing a greater burden of malaria cases. CONCLUSIONS Seasonal trends from the time series decomposition analysis show a gradual decline for both P. vivax and P. falciparum cases in West Bengal. The space-time scan statistics identified high-risk blocks for P. vivax and P. falciparum malaria and its co-occurrence. Both malaria types exhibit significant spatiotemporal variations over the study area. Identifying emerging high-risk areas of P. falciparum malaria over the Gangetic belt indicates the need for more research for its spatial shifting. Addressing the drivers of malaria transmission in these diverse clusters demands regional cooperation and strategic strategies, crucial steps towards overcoming the final obstacles in malaria eradication.
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Affiliation(s)
- Meghna Maiti
- Department of Geography, University of Calcutta, Kolkata, 700019, India.
| | - Utpal Roy
- Department of Geography, University of Calcutta, Kolkata, 700019, India
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Tokponnon TF, Ossè R, Padonou GG, Affoukou CD, Sidick A, Sewade W, Fassinou A, Koukpo CZ, Akinro B, Messenger LA, Okê M, Tchévoédé A, Ogouyemi-Hounto A, Gazard DK, Akogbeto M. Entomological Characteristics of Malaria Transmission across Benin: An Essential Element for Improved Deployment of Vector Control Interventions. INSECTS 2023; 14:52. [PMID: 36661980 PMCID: PMC9864170 DOI: 10.3390/insects14010052] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/19/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Entomological surveillance in Benin has historically been limited to zones where indoor residual spraying was performed or where long-standing sentinel surveillance sites existed. However, there are significant country-wide gaps in entomological knowledge. The National Malaria Control Program (NMCP) assessed population dynamics of Anopheles vectors and malaria transmission in each of Benin’s 12 departments to create an entomological risk profile. Two communes per department (24/77 communes) were chosen to reflect diverse geographies, ecologies and malaria prevalence. Two villages per commune were selected from which four households (HH) per village were used for human landing catches (HLCs). In each HH, an indoor and outdoor HLC occurred between 7 p.m. and 7 a.m. on two consecutive nights between July−September 2017. Captured Anopheles were identified, and ovaries were dissected to determine parous rate. Heads and thoraces were tested for Plasmodium falciparum sporozoites by ELISA. The Entomological Inoculation Rate (EIR) was calculated as the product of mosquito bite rate and sporozoite index. Bite rates from An. gambiae s.l., the primary vector species complex, differed considerably between communes; average sporozoite infection index was 3.5%. The EIR ranged from 0.02 infectious bites (ib) per human per night in the departments of Ouémé and Plateau to 1.66 ib/human/night in Collines. Based on transmission risk scales, Avrankou, Sakété and Nikki are areas of low transmission (0 < EIR < 3 ib/human/year), Adjarra, Adja Ouèrè, Zè, Toffo, Bopa, Pehunco, Pèrèrè and Kandi are of medium transmission (3 < EIR < 30 ib/human/year), and the other remaining districts are high transmission (EIR > 30 ib/human/year). The heterogeneous and diverse nature of malaria transmission in Benin was not readily apparent when only assessing entomological surveillance from sentinel sites. Prospectively, the NMCP will use study results to stratify and deploy targeted vector control interventions in districts with high EIRs to better protect populations most at-risk.
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Affiliation(s)
- Tatchémè Filémon Tokponnon
- Ministère de la Santé, Cotonou 08 BP 882, Benin
- Cotonou Entomological Research Center, Cotonou 06 BP 2604, Benin
- Centre Béninois de la Recherche Scientifique et de l’Innovation (CBRSI), Agbondjèdo, Étoile Rouge, Cotonou 03 BP 1665, Benin
- Ecole Polytechnique d’Abomey-Calavi, Université d’Abomey-Calavi, Cotonou 01 BP 2009, Benin
| | - Razaki Ossè
- Cotonou Entomological Research Center, Cotonou 06 BP 2604, Benin
| | | | | | - Aboubakar Sidick
- Cotonou Entomological Research Center, Cotonou 06 BP 2604, Benin
| | - Wilfried Sewade
- Cotonou Entomological Research Center, Cotonou 06 BP 2604, Benin
| | - Arsène Fassinou
- Cotonou Entomological Research Center, Cotonou 06 BP 2604, Benin
| | - Côme Z. Koukpo
- Cotonou Entomological Research Center, Cotonou 06 BP 2604, Benin
| | - Bruno Akinro
- Cotonou Entomological Research Center, Cotonou 06 BP 2604, Benin
| | - Louisa A. Messenger
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Department of Environmental and Occupational Health, School of Public Health, University of Nevada, Las Vegas, NV 89154, USA
| | - Mariam Okê
- Ministère de la Santé, Cotonou 08 BP 882, Benin
| | | | - Aurore Ogouyemi-Hounto
- National Malaria Control Program, Cotonou 01 BP 882, Benin
- Parasitology-Mycologie Research Unit, Faculté des Sciences de la Santé, University of Abomey-Calavi, Cotonou 01 BP 188, Benin
| | - Dorothée Kinde Gazard
- Parasitology-Mycologie Research Unit, Faculté des Sciences de la Santé, University of Abomey-Calavi, Cotonou 01 BP 188, Benin
| | - Martin Akogbeto
- Cotonou Entomological Research Center, Cotonou 06 BP 2604, Benin
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Golumbeanu M, Yang GJ, Camponovo F, Stuckey EM, Hamon N, Mondy M, Rees S, Chitnis N, Cameron E, Penny MA. Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions. Infect Dis Poverty 2022; 11:61. [PMID: 35659301 PMCID: PMC9167503 DOI: 10.1186/s40249-022-00981-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 05/04/2022] [Indexed: 01/04/2023] Open
Abstract
Background Substantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning. Our analysis identifies requirements of efficacy, coverage, and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence. Methods A mathematical model of malaria transmission dynamics is used to simulate deployment and predict potential impact of new malaria interventions by considering operational, health-system, population, and disease characteristics. Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications. We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals. Results We apply our approach to five malaria interventions under development. Aiming for malaria prevalence reduction, we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals. While coverage is generally identified as the largest driver of impact, higher efficacy, longer protection duration or multiple deployments per year are needed to increase prevalence reduction. We show that interventions on multiple parasite or vector targets, as well as combinations the new interventions with drug treatment, lead to significant burden reductions and lower efficacy or duration requirements. Conclusions Our approach uses disease dynamic models and machine learning to support decision-making and resource investment, facilitating development of new malaria interventions. By evaluating the intervention capabilities in relation to the targeted health goal, our analysis allows prioritization of interventions and of their specifications from an early stage in development, and subsequent investments to be channeled cost-effectively towards impact maximization. This study highlights the role of mathematical models to support intervention development. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions. Graphical abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s40249-022-00981-1.
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Affiliation(s)
- Monica Golumbeanu
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.,University of Basel, Basel, Switzerland
| | - Guo-Jing Yang
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.,Key Laboratory of Tropical Translational Medicine of Ministry of Education and School of Tropical Medicine and Laboratory Medicine, The First and Second Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, Hainan, People's Republic of China.,University of Basel, Basel, Switzerland
| | - Flavia Camponovo
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.,University of Basel, Basel, Switzerland.,Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | | | | | | | - Sarah Rees
- Innovative Vector Control Consortium, Liverpool, UK
| | - Nakul Chitnis
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.,University of Basel, Basel, Switzerland
| | - Ewan Cameron
- Malaria Atlas Project, Big Data Institute, University of Oxford, Oxford, UK.,Curtin University, Perth, Australia.,Telethon Kids Institute, Perth Children's Hospital, Perth, Australia
| | - Melissa A Penny
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland. .,University of Basel, Basel, Switzerland.
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Runge M, Mapua S, Nambunga I, Smith TA, Chitnis N, Okumu F, Pothin E. Evaluation of different deployment strategies for larviciding to control malaria: a simulation study. Malar J 2021; 20:324. [PMID: 34315473 PMCID: PMC8314573 DOI: 10.1186/s12936-021-03854-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 07/16/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Larviciding against malaria vectors in Africa has been limited to indoor residual spraying and insecticide-treated nets, but is increasingly being considered by some countries as a complementary strategy. However, despite progress towards improved larvicides and new tools for mapping or treating mosquito-breeding sites, little is known about the optimal deployment strategies for larviciding in different transmission and seasonality settings. METHODS A malaria transmission model, OpenMalaria, was used to simulate varying larviciding strategies and their impact on host-seeking mosquito densities, entomological inoculation rate (EIR) and malaria prevalence. Variations in coverage, duration, frequency, and timing of larviciding were simulated for three transmission intensities and four transmission seasonality profiles. Malaria transmission was assumed to follow rainfall with a lag of one month. Theoretical sub-Saharan African settings with Anopheles gambiae as the dominant vector were chosen to explore impact. Relative reduction compared to no larviciding was predicted for each indicator during the simulated larviciding period. RESULTS Larviciding immediately reduced the predicted host-seeking mosquito densities and EIRs to a maximum that approached or exceeded the simulated coverage. Reduction in prevalence was delayed by approximately one month. The relative reduction in prevalence was up to four times higher at low than high transmission. Reducing larviciding frequency (i.e., from every 5 to 10 days) resulted in substantial loss in effectiveness (54, 45 and 53% loss of impact for host-seeking mosquito densities, EIR and prevalence, respectively). In seasonal settings the most effective timing of larviciding was during or at the beginning of the rainy season and least impactful during the dry season, assuming larviciding deployment for four months. CONCLUSION The results highlight the critical role of deployment strategies on the impact of larviciding. Overall, larviciding would be more effective in settings with low and seasonal transmission, and at the beginning and during the peak densities of the target species populations. For maximum impact, implementers should consider the practical ranges of coverage, duration, frequency, and timing of larviciding in their respective contexts. More operational data and improved calibration would enable models to become a practical tool to support malaria control programmes in developing larviciding strategies that account for the diversity of contexts.
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Affiliation(s)
- Manuela Runge
- Swiss Tropical and Public Health Institute, Basel, Switzerland. .,University of Basel, Basel, Switzerland.
| | - Salum Mapua
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Ifakara, Tanzania
| | - Ismail Nambunga
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Ifakara, Tanzania
| | - Thomas A Smith
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Nakul Chitnis
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Fredros Okumu
- Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Ifakara, Tanzania
| | - Emilie Pothin
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland.,Clinton Health Access Initiative, Boston, USA
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Space-Time Clustering Characteristics of Malaria in Bhutan at the End Stages of Elimination. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18115553. [PMID: 34067393 PMCID: PMC8196969 DOI: 10.3390/ijerph18115553] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 05/16/2021] [Accepted: 05/17/2021] [Indexed: 01/12/2023]
Abstract
Malaria in Bhutan has fallen significantly over the last decade. As Bhutan attempts to eliminate malaria in 2022, this study aimed to characterize the space-time clustering of malaria from 2010 to 2019. Malaria data were obtained from the Bhutan Vector-Borne Disease Control Program data repository. Spatial and space-time cluster analyses of Plasmodium falciparum and Plasmodium vivax cases were conducted at the sub-district level from 2010 to 2019 using Kulldorff's space-time scan statistic. A total of 768 confirmed malaria cases, including 454 (59%) P. vivax cases, were reported in Bhutan during the study period. Significant temporal clusters of cases caused by both species were identified between April and September. The most likely spatial clusters were detected in the central part of Bhutan throughout the study period. The most likely space-time cluster was in Sarpang District and neighboring districts between January 2010 to June 2012 for cases of infection with both species. The most likely cluster for P. falciparum infection had a radius of 50.4 km and included 26 sub-districts with a relative risk (RR) of 32.7. The most likely cluster for P. vivax infection had a radius of 33.6 km with 11 sub-districts and RR of 27.7. Three secondary space-time clusters were detected in other parts of Bhutan. Spatial and space-time cluster analysis identified high-risk areas and periods for both P. vivax and P. falciparum malaria. Both malaria types showed significant spatial and spatiotemporal variations. Operational research to understand the drivers of residual transmission in hotspot sub-districts will help to overcome the final challenges of malaria elimination in Bhutan.
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Nguyen M, Howes RE, Lucas TCD, Battle KE, Cameron E, Gibson HS, Rozier J, Keddie S, Collins E, Arambepola R, Kang SY, Hendriks C, Nandi A, Rumisha SF, Bhatt S, Mioramalala SA, Nambinisoa MA, Rakotomanana F, Gething PW, Weiss DJ. Mapping malaria seasonality in Madagascar using health facility data. BMC Med 2020; 18:26. [PMID: 32036785 PMCID: PMC7008536 DOI: 10.1186/s12916-019-1486-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 12/20/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions. Identifying location-specific seasonality characteristics is useful for planning interventions. While most existing maps of malaria seasonality use fixed thresholds of rainfall, temperature, and/or vegetation indices to identify suitable transmission months, we construct a statistical modelling framework for characterising the seasonal patterns derived directly from monthly health facility data. METHODS With data from 2669 of the 3247 health facilities in Madagascar, a spatiotemporal regression model was used to estimate seasonal patterns across the island. In the absence of catchment population estimates or the ability to aggregate to the district level, this focused on the monthly proportions of total annual cases by health facility level. The model was informed by dynamic environmental covariates known to directly influence seasonal malaria trends. To identify operationally relevant characteristics such as the transmission start months and associated uncertainty measures, an algorithm was developed and applied to model realisations. A seasonality index was used to incorporate burden information from household prevalence surveys and summarise 'how seasonal' locations are relative to their surroundings. RESULTS Positive associations were detected between monthly case proportions and temporally lagged covariates of rainfall and temperature suitability. Consistent with the existing literature, model estimates indicate that while most parts of Madagascar experience peaks in malaria transmission near March-April, the eastern coast experiences an earlier peak around February. Transmission was estimated to start in southeast districts before southwest districts, suggesting that indoor residual spraying should be completed in the same order. In regions where the data suggested conflicting seasonal signals or two transmission seasons, estimates of seasonal features had larger deviations and therefore less certainty. CONCLUSIONS Monthly health facility data can be used to establish seasonal patterns in malaria burden and augment the information provided by household prevalence surveys. The proposed modelling framework allows for evidence-based and cohesive inferences on location-specific seasonal characteristics. As health surveillance systems continue to improve, it is hoped that more of such data will be available to improve our understanding and planning of intervention strategies.
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Affiliation(s)
- Michele Nguyen
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
| | - Rosalind E Howes
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tim C D Lucas
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Katherine E Battle
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Ewan Cameron
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Harry S Gibson
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jennifer Rozier
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Suzanne Keddie
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Emma Collins
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Rohan Arambepola
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Su Yun Kang
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Chantal Hendriks
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Anita Nandi
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Susan F Rumisha
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Samir Bhatt
- Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | | | | | | | - Peter W Gething
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Daniel J Weiss
- Malaria Atlas Project, Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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Chitnis N, Schapira A, Schindler C, Penny MA, Smith TA. Mathematical analysis to prioritise strategies for malaria elimination. J Theor Biol 2018; 455:118-130. [PMID: 30006002 PMCID: PMC6117457 DOI: 10.1016/j.jtbi.2018.07.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 06/21/2018] [Accepted: 07/09/2018] [Indexed: 11/27/2022]
Abstract
Malaria and some other tropical diseases are currently targeted for elimination and eventually eradication. Since resources are limited, prioritisation of countries or areas for elimination is often necessary. However, this prioritisation is frequently conducted in an ad hoc manner. Lower transmission areas are usually targeted for elimination first, but for some areas this necessitates long and potentially expensive surveillance programs while transmission is eliminated from neighbouring higher transmission areas. We use a mathematical model to compare the implications of prioritisation choices in reducing overall burden and costs. We show that when the duration of the elimination program is independent of the transmission potential, burden is always reduced most by targeting high transmission areas first, but to reduce costs the optimal ordering depends on the actual transmission levels. In general, when overall transmission potential is low and the surveillance cost per secondary case compared to the cost per imported case is low, targeting the higher transmission area for elimination first is favoured.
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Affiliation(s)
- Nakul Chitnis
- Swiss Tropical and Public Health Institute, Socinstrasse 57, Basel 4002, Switzerland; University of Basel, Basel 4003, Switzerland.
| | - Allan Schapira
- Swiss Tropical and Public Health Institute, Socinstrasse 57, Basel 4002, Switzerland; University of Basel, Basel 4003, Switzerland
| | - Christian Schindler
- Swiss Tropical and Public Health Institute, Socinstrasse 57, Basel 4002, Switzerland; University of Basel, Basel 4003, Switzerland
| | - Melissa A Penny
- Swiss Tropical and Public Health Institute, Socinstrasse 57, Basel 4002, Switzerland; University of Basel, Basel 4003, Switzerland
| | - Thomas A Smith
- Swiss Tropical and Public Health Institute, Socinstrasse 57, Basel 4002, Switzerland; University of Basel, Basel 4003, Switzerland
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Coutinho PEG, Candido LA, Tadei WP, da Silva Junior UL, Correa HKM. An analysis of the influence of the local effects of climatic and hydrological factors affecting new malaria cases in riverine areas along the Rio Negro and surrounding Puraquequara Lake, Amazonas, Brazil. ENVIRONMENTAL MONITORING AND ASSESSMENT 2018; 190:311. [PMID: 29700629 DOI: 10.1007/s10661-018-6677-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 04/06/2018] [Indexed: 06/08/2023]
Abstract
A study was conducted at three sampling regions along the Rio Negro and surrounding Puraquequara Lake, Amazonas, Brazil. The aim was to determine the influence of the local effects of climatic and hydrological variables on new malaria cases. Data was gathered on the river level, precipitation, air temperature, and the number of new cases of autochthonous malaria between January 2003 and December 2013. Monthly averages, time series decompositions, cross-correlations, and multiple regressions revealed different relationships at each location. The sampling region in the upper Rio Negro indicated no statistically significant results. However, monthly averages suggest that precipitation and air temperature correlate positively with the occurrence of new cases of malaria. In the mid Rio Negro and Puraquequara Lake, the river level positively correlated, and temperature negatively correlated with new transmissions, while precipitation correlated negatively in the mid Rio Negro and positively on the lake. Overall, the river level is a key variable affecting the formation of breeding sites, while precipitation may either develop or damage them. A negative temperature correlation is associated with the occurrence of new annual post-peak cases of malaria, when the monthly average exceeds 28.5 °C. This suggests that several factors contribute to the occurrence of new malaria cases as higher temperatures are reached at the same time as precipitation and the river levels are lowest. Differences between signals and correlation lags indicate that local characteristics have an impact on how different variables influence the disease vector's life cycle, pathogens, and consequently, new cases of malaria.
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Affiliation(s)
- Paulo Eduardo Guzzo Coutinho
- Nucleus of Research Support in Para (Núcleo de Apoio à Pesquisa no Pará (INPA/Nappa/Santarém)), National Institute of Amazon Researches (Instituto Nacional de Pesquisas da Amazônia), Rua 24 de outubro, 3289, Salé, Santarém, Pará, 68040-010, Brazil.
| | - Luiz Antonio Candido
- INPA/CAMPUS 2 (INPA/CAMPUS 2), National Institute of Amazon Researches (Instituto Nacional de Pesquisas da Amazônia), Prédio LBA, sala da Coordenação de Dinâmica Ambiental Av. André Araújo, 2936, Aleixo, Manaus, Amazonas, 69060-001, Brazil
| | - Wanderli Pedro Tadei
- INPA/CAMPUS 1 - Malaria and Dengue Laboratory (INPA/CAMPUS 1 - Laboratório de Malária e Dengue), National Institute of Amazon Researches (Instituto Nacional de Pesquisas da Amazônia), Av. André Araújo, 2936, Aleixo, Manaus, Amazonas, 69060-001, Brazil
| | - Urbano Lopes da Silva Junior
- National Center for Research and Conservation of Amazonian Biodiversity (Centro Nacional de Pesquisa e Conservação da Biodiversidade Amazônica (Cepam/ICMBio)), Chico Mendes Institute for Biodiversity Conservation (Instituto Chico Mendes de Conservação da Biodiversidade), UFAM, Campus Universitário Arthur Virgílio Filho setor sul, Av. Gal Rodrigo Otávio Jordão Ramos, 6200, Coroado, Manaus, 69077-000, Brazil
| | - Honorly Katia Mestre Correa
- Institute of Educational Science (Instituto de Ciências da Educação (ICED/UFOPA)), Federal University of Western Para (Universidade Federal do Oeste do Prá), Av. Marechal Rondon, s/n, Caranazal, Santarem, Para, 68040-070, Brazil
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Erraguntla M, Zapletal J, Lawley M. Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management. Health Informatics J 2017; 25:1170-1187. [PMID: 29278956 DOI: 10.1177/1460458217747112] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions.
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Korenromp E, Hamilton M, Sanders R, Mahiané G, Briët OJT, Smith T, Winfrey W, Walker N, Stover J. Impact of malaria interventions on child mortality in endemic African settings: comparison and alignment between LiST and Spectrum-Malaria model. BMC Public Health 2017; 17:781. [PMID: 29143637 PMCID: PMC5688465 DOI: 10.1186/s12889-017-4739-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Background In malaria-endemic countries, malaria prevention and treatment are critical for child health. In the context of intervention scale-up and rapid changes in endemicity, projections of intervention impact and optimized program scale-up strategies need to take into account the consequent dynamics of transmission and immunity. Methods The new Spectrum-Malaria program planning tool was used to project health impacts of Insecticide-Treated mosquito Nets (ITNs) and effective management of uncomplicated malaria cases (CMU), among other interventions, on malaria infection prevalence, case incidence and mortality in children 0–4 years, 5–14 years of age and adults. Spectrum-Malaria uses statistical models fitted to simulations of the dynamic effects of increasing intervention coverage on these burdens as a function of baseline malaria endemicity, seasonality in transmission and malaria intervention coverage levels (estimated for years 2000 to 2015 by the World Health Organization and Malaria Atlas Project). Spectrum-Malaria projections of proportional reductions in under-five malaria mortality were compared with those of the Lives Saved Tool (LiST) for the Democratic Republic of the Congo and Zambia, for given (standardized) scenarios of ITN and/or CMU scale-up over 2016–2030. Results Proportional mortality reductions over the first two years following scale-up of ITNs from near-zero baselines to moderately higher coverages align well between LiST and Spectrum-Malaria —as expected since both models were fitted to cluster-randomized ITN trials in moderate-to-high-endemic settings with 2-year durations. For further scale-up from moderately high ITN coverage to near-universal coverage (as currently relevant for strategic planning for many countries), Spectrum-Malaria predicts smaller additional ITN impacts than LiST, reflecting progressive saturation. For CMU, especially in the longer term (over 2022–2030) and for lower-endemic settings (like Zambia), Spectrum-Malaria projects larger proportional impacts, reflecting onward dynamic effects not fully captured by LiST. Conclusions Spectrum-Malaria complements LiST by extending the scope of malaria interventions, program packages and health outcomes that can be evaluated for policy making and strategic planning within and beyond the perspective of child survival. Electronic supplementary material The online version of this article (10.1186/s12889-017-4739-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - Matthew Hamilton
- Avenir Health, 655 Winding Brook Drive, Glastonbury, CT-06033, USA
| | - Rachel Sanders
- Avenir Health, 655 Winding Brook Drive, Glastonbury, CT-06033, USA
| | - Guy Mahiané
- Avenir Health, 655 Winding Brook Drive, Glastonbury, CT-06033, USA
| | - Olivier J T Briët
- Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051, Basel, Switzerland.,Epidemiology and Public Health, University of Basel, Basel, Switzerland
| | - Thomas Smith
- Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051, Basel, Switzerland.,Epidemiology and Public Health, University of Basel, Basel, Switzerland
| | - William Winfrey
- Avenir Health, 655 Winding Brook Drive, Glastonbury, CT-06033, USA
| | - Neff Walker
- Department of International Health, Institute for International Programs, Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD, 21205, USA
| | - John Stover
- Avenir Health, 655 Winding Brook Drive, Glastonbury, CT-06033, USA
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Korenromp E, Mahiané G, Hamilton M, Pretorius C, Cibulskis R, Lauer J, Smith TA, Briët OJT. Malaria intervention scale-up in Africa: effectiveness predictions for health programme planning tools, based on dynamic transmission modelling. Malar J 2016; 15:417. [PMID: 27538889 PMCID: PMC4991118 DOI: 10.1186/s12936-016-1461-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2016] [Accepted: 07/29/2016] [Indexed: 12/22/2022] Open
Abstract
Background Scale-up of malaria prevention and treatment needs to continue to further important gains made in the past decade, but national strategies and budget allocations are not always evidence-based. Statistical models were developed summarizing dynamically simulated relations between increases in coverage and intervention impact, to inform a malaria module in the Spectrum health programme planning tool. Methods The dynamic Plasmodiumfalciparum transmission model OpenMalaria was used to simulate health effects of scale-up of insecticide-treated net (ITN) usage, indoor residual spraying (IRS), management of uncomplicated malaria cases (CM) and seasonal malaria chemoprophylaxis (SMC) over a 10-year horizon, over a range of settings with stable endemic malaria. Generalized linear regression models (GLMs) were used to summarize determinants of impact across a range of sub-Sahara African settings. Results Selected (best) GLMs explained 94–97 % of variation in simulated post-intervention parasite infection prevalence, 86–97 % of variation in case incidence (three age groups, three 3-year horizons), and 74–95 % of variation in malaria mortality. For any given effective population coverage, CM and ITNs were predicted to avert most prevalent infections, cases and deaths, with lower impacts for IRS, and impacts of SMC limited to young children reached. Proportional impacts were larger at lower endemicity, and (except for SMC) largest in low-endemic settings with little seasonality. Incremental health impacts for a given coverage increase started to diminish noticeably at above ~40 % coverage, while in high-endemic settings, CM and ITNs acted in synergy by lowering endemicity. Vector control and CM, by reducing endemicity and acquired immunity, entail a partial rebound in malaria mortality among people above 5 years of age from around 5–7 years following scale-up. SMC does not reduce endemicity, but slightly shifts malaria to older ages by reducing immunity in child cohorts reached. Conclusion Health improvements following malaria intervention scale-up vary with endemicity, seasonality, age and time. Statistical models can emulate epidemiological dynamics and inform strategic planning and target setting for malaria control. Electronic supplementary material The online version of this article (doi:10.1186/s12936-016-1461-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | | | | | | | - Richard Cibulskis
- World Health Organization Global Malaria Programme, Geneva, Switzerland
| | - Jeremy Lauer
- World Health Organization Health Systems Governance and Financing dept., Geneva, Switzerland
| | - Thomas A Smith
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Olivier J T Briët
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
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12
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Distribution of malaria exposure in endemic countries in Africa considering country levels of effective treatment. Malar J 2015; 14:384. [PMID: 26437798 PMCID: PMC4595196 DOI: 10.1186/s12936-015-0864-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Accepted: 08/23/2015] [Indexed: 11/10/2022] Open
Abstract
Background Malaria prevalence, clinical incidence, treatment, and transmission rates are dynamically interrelated. Prevalence is often considered a measure of malaria transmission, but treatment of clinical malaria reduces prevalence, and consequently also infectiousness to the mosquito vector and onward transmission. The impact of the frequency of treatment on prevalence in a population is generally not considered. This can lead to potential underestimation of malaria exposure in settings with good health systems. Furthermore, these dynamical relationships between prevalence, treatment, and transmission have not generally been taken into account in estimates of burden. Methods Using prevalence as an input, estimates of disease incidence and transmission [as the distribution of the entomological inoculation rate (EIR)] for Plasmodium falciparum have now been made for 43 countries in Africa using both empirical relationships (that do not allow for treatment) and OpenMalaria dynamic micro-simulation models (that explicitly include the effects of treatment). For each estimate, prevalence inputs were taken from geo-statistical models fitted for the year 2010 by the Malaria Atlas Project to all available observed prevalence data. National level estimates of the effectiveness of case management in treating clinical attacks were used as inputs to the estimation of both EIR and disease incidence by the dynamic models. Results and conclusions When coverage of effective treatment is taken into account, higher country level estimates of average EIR and thus higher disease burden, are obtained for a given prevalence level, especially where access to treatment is high, and prevalence relatively low. These methods provide a unified framework for comparison of both the immediate and longer-term impacts of case management and of preventive interventions. Electronic supplementary material The online version of this article (doi:10.1186/s12936-015-0864-3) contains supplementary material, which is available to authorized users.
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Defining the relationship between infection prevalence and clinical incidence of Plasmodium falciparum malaria. Nat Commun 2015; 6:8170. [PMID: 26348689 PMCID: PMC4569718 DOI: 10.1038/ncomms9170] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Accepted: 07/24/2015] [Indexed: 01/08/2023] Open
Abstract
In many countries health system data remain too weak to accurately enumerate Plasmodium falciparum malaria cases. In response, cartographic approaches have been developed that link maps of infection prevalence with mathematical relationships to predict the incidence rate of clinical malaria. Microsimulation (or ‘agent-based') models represent a powerful new paradigm for defining such relationships; however, differences in model structure and calibration data mean that no consensus yet exists on the optimal form for use in disease-burden estimation. Here we develop a Bayesian statistical procedure combining functional regression-based model emulation with Markov Chain Monte Carlo sampling to calibrate three selected microsimulation models against a purpose-built data set of age-structured prevalence and incidence counts. This allows the generation of ensemble forecasts of the prevalence–incidence relationship stratified by age, transmission seasonality, treatment level and exposure history, from which we predict accelerating returns on investments in large-scale intervention campaigns as transmission and prevalence are progressively reduced. Mathematical models are used to predict malaria burden to inform disease control efforts. Here, Cameron et al. use Bayesian statistics to calibrate previous models against a data set of age-structured prevalence and incidence, generating stratified forecasts of the prevalence–incidence relationship.
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Srimath-Tirumula-Peddinti RCPK, Neelapu NRR, Sidagam N. Association of Climatic Variability, Vector Population and Malarial Disease in District of Visakhapatnam, India: A Modeling and Prediction Analysis. PLoS One 2015; 10:e0128377. [PMID: 26110279 PMCID: PMC4482491 DOI: 10.1371/journal.pone.0128377] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2014] [Accepted: 04/26/2015] [Indexed: 01/02/2023] Open
Abstract
Background Malarial incidence, severity, dynamics and distribution of malaria are strongly determined by climatic factors, i.e., temperature, precipitation, and relative humidity. The objectives of the current study were to analyse and model the relationships among climate, vector and malaria disease in district of Visakhapatnam, India to understand malaria transmission mechanism (MTM). Methodology Epidemiological, vector and climate data were analysed for the years 2005 to 2011 in Visakhapatnam to understand the magnitude, trends and seasonal patterns of the malarial disease. Statistical software MINITAB ver. 14 was used for performing correlation, linear and multiple regression analysis. Results/Findings Perennial malaria disease incidence and mosquito population was observed in the district of Visakhapatnam with peaks in seasons. All the climatic variables have a significant influence on disease incidence as well as on mosquito populations. Correlation coefficient analysis, seasonal index and seasonal analysis demonstrated significant relationships among climatic factors, mosquito population and malaria disease incidence in the district of Visakhapatnam, India. Multiple regression and ARIMA (I) models are best suited models for modeling and prediction of disease incidences and mosquito population. Predicted values of average temperature, mosquito population and malarial cases increased along with the year. Developed MTM algorithm observed a major MTM cycle following the June to August rains and occurring between June to September and minor MTM cycles following March to April rains and occurring between March to April in the district of Visakhapatnam. Fluctuations in climatic factors favored an increase in mosquito populations and thereby increasing the number of malarial cases. Rainfall, temperatures (20°C to 33°C) and humidity (66% to 81%) maintained a warmer, wetter climate for mosquito growth, parasite development and malaria transmission. Conclusions/Significance Changes in climatic factors influence malaria directly by modifying the behaviour and geographical distribution of vectors and by changing the length of the life cycle of the parasite.
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Affiliation(s)
| | - Nageswara Rao Reddy Neelapu
- Department of Biochemistry and Bioinformatics, GITAM Institute of Science, GITAM University, Rushikonda Campus, Visakhapatnam, Andhra Pradesh, India
| | - Naresh Sidagam
- Department of Statistics, College of Science and Technology, Andhra University, Waltair, Visakhapatnam, Andhra Pradesh, India
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