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Bertozzi-Villa A, Bever CA, Gerardin J, Proctor JL, Wu M, Harding D, Hollingsworth TD, Bhatt S, Gething PW. An archetypes approach to malaria intervention impact mapping: a new framework and example application. Malar J 2023; 22:138. [PMID: 37101269 PMCID: PMC10131392 DOI: 10.1186/s12936-023-04535-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/15/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND As both mechanistic and geospatial malaria modeling methods become more integrated into malaria policy decisions, there is increasing demand for strategies that combine these two methods. This paper introduces a novel archetypes-based methodology for generating high-resolution intervention impact maps based on mechanistic model simulations. An example configuration of the framework is described and explored. METHODS First, dimensionality reduction and clustering techniques were applied to rasterized geospatial environmental and mosquito covariates to find archetypal malaria transmission patterns. Next, mechanistic models were run on a representative site from each archetype to assess intervention impact. Finally, these mechanistic results were reprojected onto each pixel to generate full maps of intervention impact. The example configuration used ERA5 and Malaria Atlas Project covariates, singular value decomposition, k-means clustering, and the Institute for Disease Modeling's EMOD model to explore a range of three-year malaria interventions primarily focused on vector control and case management. RESULTS Rainfall, temperature, and mosquito abundance layers were clustered into ten transmission archetypes with distinct properties. Example intervention impact curves and maps highlighted archetype-specific variation in efficacy of vector control interventions. A sensitivity analysis showed that the procedure for selecting representative sites to simulate worked well in all but one archetype. CONCLUSION This paper introduces a novel methodology which combines the richness of spatiotemporal mapping with the rigor of mechanistic modeling to create a multi-purpose infrastructure for answering a broad range of important questions in the malaria policy space. It is flexible and adaptable to a range of input covariates, mechanistic models, and mapping strategies and can be adapted to the modelers' setting of choice.
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Affiliation(s)
- Amelia Bertozzi-Villa
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, USA.
- Malaria Atlas Project, Telethon Kids Institute, Perth, Australia.
- Big Data Institute, Nuffield Department of Medicine, Oxford University, Oxford, UK.
| | - Caitlin A Bever
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, USA
| | - Jaline Gerardin
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, USA
- Department of Preventive Medicine and Institute for Global Health, Northwestern University, Chicago, USA
| | - Joshua L Proctor
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, USA
| | - Meikang Wu
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, USA
| | - Dennis Harding
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, USA
| | | | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College, London, UK
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Peter W Gething
- Malaria Atlas Project, Telethon Kids Institute, Perth, Australia
- Curtin University, Perth, Australia
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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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3
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Runge M, Thawer SG, Molteni F, Chacky F, Mkude S, Mandike R, Snow RW, Lengeler C, Mohamed A, Pothin E. Sub-national tailoring of malaria interventions in Mainland Tanzania: simulation of the impact of strata-specific intervention combinations using modelling. Malar J 2022; 21:92. [PMID: 35300707 PMCID: PMC8929286 DOI: 10.1186/s12936-022-04099-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 02/23/2022] [Indexed: 11/21/2022] Open
Abstract
Background To accelerate progress against malaria in high burden countries, a strategic reorientation of resources at the sub-national level is needed. This paper describes how mathematical modelling was used in mainland Tanzania to support the strategic revision that followed the mid-term review of the 2015–2020 national malaria strategic plan (NMSP) and the epidemiological risk stratification at the council level in 2018. Methods Intervention mixes, selected by the National Malaria Control Programme, were simulated for each malaria risk strata per council. Intervention mixes included combinations of insecticide-treated bed nets (ITN), indoor residual spraying, larval source management, and intermittent preventive therapies for school children (IPTsc). Effective case management was either based on estimates from the malaria indicator survey in 2016 or set to a hypothetical target of 85%. A previously calibrated mathematical model in OpenMalaria was used to compare intervention impact predictions for prevalence and incidence between 2016 and 2020, or 2022. Results For each malaria risk stratum four to ten intervention mixes were explored. In the low-risk and urban strata, the scenario without a ITN mass campaign in 2019, predicted high increase in prevalence by 2020 and 2022, while in the very-low strata the target prevalence of less than 1% was maintained at low pre-intervention transmission intensity and high case management. In the moderate and high strata, IPTsc in addition to existing vector control was predicted to reduce the incidence by an additional 15% and prevalence by 22%. In the high-risk strata, all interventions together reached a maximum reduction of 76%, with around 70% of that reduction attributable to high case management and ITNs. Overall, the simulated revised NMSP was predicted to achieve a slightly lower prevalence in 2020 compared to the 2015–2020 NMSP (5.3% vs 6.3%). Conclusion Modelling supported the choice of intervention per malaria risk strata by providing impact comparisons of various alternative intervention mixes to address specific questions relevant to the country. The use of a council-calibrated model, that reproduces local malaria trends, represents a useful tool for compiling available evidence into a single analytical platform, that complement other evidence, to aid national programmes with decision-making processes. Supplementary Information The online version contains supplementary material available at 10.1186/s12936-022-04099-5.
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Affiliation(s)
- Manuela Runge
- Swiss Tropical and Public Health Institute, Basel, Switzerland. .,University of Basel, Basel, Switzerland.
| | - Sumaiyya G Thawer
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Fabrizio Molteni
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Frank Chacky
- National Malaria Control Programme, Dodoma, Tanzania.,Ministry of Health, Community Development, Gender, Elderly, and Children, Dodoma, Tanzania
| | - Sigsbert Mkude
- National Malaria Control Programme, Dodoma, Tanzania.,Ministry of Health, Community Development, Gender, Elderly, and Children, Dodoma, Tanzania
| | - Renata Mandike
- National Malaria Control Programme, Dodoma, Tanzania.,Ministry of Health, Community Development, Gender, Elderly, and Children, Dodoma, Tanzania
| | - Robert W Snow
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.,Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
| | - Christian Lengeler
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Ally Mohamed
- National Malaria Control Programme, Dodoma, Tanzania.,Ministry of Health, Community Development, Gender, Elderly, and Children, Dodoma, Tanzania
| | - Emilie Pothin
- Swiss Tropical and Public Health Institute, Basel, Switzerland. .,University of Basel, Basel, Switzerland. .,CHAI, Clinton Health Access Initiative, New York, USA.
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4
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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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Runge M, Snow RW, Molteni F, Thawer S, Mohamed A, Mandike R, Giorgi E, Macharia PM, Smith TA, Lengeler C, Pothin E. Simulating the council-specific impact of anti-malaria interventions: A tool to support malaria strategic planning in Tanzania. PLoS One 2020; 15:e0228469. [PMID: 32074112 PMCID: PMC7029840 DOI: 10.1371/journal.pone.0228469] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 01/16/2020] [Indexed: 11/25/2022] Open
Abstract
INTRODUCTION The decision-making process for malaria control and elimination strategies has become more challenging. Interventions need to be targeted at council level to allow for changing malaria epidemiology and an increase in the number of possible interventions. Models of malaria dynamics can support this process by simulating potential impacts of multiple interventions in different settings and determining appropriate packages of interventions for meeting specific expected targets. METHODS The OpenMalaria model of malaria dynamics was calibrated for all 184 councils in mainland Tanzania using data from malaria indicator surveys, school parasitaemia surveys, entomological surveillance, and vector control deployment data. The simulations were run for different transmission intensities per region and five interventions, currently or potentially included in the National Malaria Strategic Plan, individually and in combination. The simulated prevalences were fitted to council specific prevalences derived from geostatistical models to obtain council specific predictions of the prevalence and number of cases between 2017 and 2020. The predictions were used to evaluate in silico the feasibility of the national target of reaching a prevalence of below 1% by 2020, and to suggest alternative intervention stratifications for the country. RESULTS The historical prevalence trend was fitted for each council with an agreement of 87% in 2016 (95%CI: 0.84-0.90) and an agreement of 90% for the historical trend (2003-2016) (95%CI: 0.87-0.93) The current national malaria strategy was expected to reduce the malaria prevalence between 2016 and 2020 on average by 23.8% (95% CI: 19.7%-27.9%) if current case management levels were maintained, and by 52.1% (95% CI: 48.8%-55.3%) if the case management were improved. Insecticide treated nets and case management were the most cost-effective interventions, expected to reduce the prevalence by 25.0% (95% CI: 19.7%-30.2) and to avert 37 million cases between 2017 and 2020. Mass drug administration was included in most councils in the stratification selected for meeting the national target at minimal costs, expected to reduce the prevalence by 77.5% (95%CI: 70.5%-84.5%) and to avert 102 million cases, with almost twice higher costs than those of the current national strategy. In summary, the model suggested that current interventions are not sufficient to reach the national aim of a prevalence of less than 1% by 2020 and a revised strategic plan needs to consider additional, more effective interventions, especially in high transmission areas and that the targets need to be revisited. CONCLUSION The methodology reported here is based on intensive interactions with the NMCP and provides a helpful tool for assessing the feasibility of country specific targets and for determining which intervention stratifications at sub-national level will have most impact. This country-led application could support strategic planning of malaria control in many other malaria endemic countries.
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Affiliation(s)
- Manuela Runge
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Robert W. Snow
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, England, United Kingodm
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
| | - Fabrizio Molteni
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- National Malaria Control Programme (NMCP), Dar es Salaam, Tanzania
| | - Sumaiyya Thawer
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- National Malaria Control Programme (NMCP), Dar es Salaam, Tanzania
| | - Ally Mohamed
- National Malaria Control Programme (NMCP), Dar es Salaam, Tanzania
| | - Renata Mandike
- National Malaria Control Programme (NMCP), Dar es Salaam, Tanzania
| | - Emanuele Giorgi
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, England, United Kingodm
| | - Peter M. Macharia
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
| | - Thomas A. Smith
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Christian Lengeler
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Emilie Pothin
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
- Clinton Health Access Initiative, Boston, Massachusetts, United States of America
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Ng'habi K, Viana M, Matthiopoulos J, Lyimo I, Killeen G, Ferguson HM. Mesocosm experiments reveal the impact of mosquito control measures on malaria vector life history and population dynamics. Sci Rep 2018; 8:13949. [PMID: 30224714 PMCID: PMC6141522 DOI: 10.1038/s41598-018-31805-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 08/24/2018] [Indexed: 11/29/2022] Open
Abstract
The impact of control measures on mosquito vector fitness and demography is usually estimated from bioassays or indirect variables in the field. Whilst indicative, neither approach is sufficient to quantify the potentially complex response of mosquito populations to combined interventions. Here, large replicated mesocosms were used to measure the population-level response of the malaria vector Anopheles arabiensis to long-lasting insecticidal nets (LLINs) when used in isolation, or combined with insecticidal eave louvers (EL), or treatment of cattle with the endectocide Ivermectin (IM). State-space models (SSM) were fit to these experimental data, revealing that LLIN introduction reduced adult mosquito survival by 91% but allowed population persistence. ELs provided no additional benefit, but IM reduced mosquito fecundity by 59% and nearly eliminated all populations when combined with LLINs. This highlights the value of IM for integrated vector control, and mesocosm population experiments combined with SSM for identifying optimal combinations for vector population elimination.
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Affiliation(s)
- Kija Ng'habi
- Ifakara Health Institute, Environmental Health and Ecological Sciences, Ifakara, United Republic of Tanzania
- School of Health Sciences, University of Dar es Salaam, Dar es Salaam, Tanzania
| | - Mafalda Viana
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
| | - Jason Matthiopoulos
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
| | - Issa Lyimo
- Ifakara Health Institute, Environmental Health and Ecological Sciences, Ifakara, United Republic of Tanzania
| | - Gerry Killeen
- Ifakara Health Institute, Environmental Health and Ecological Sciences, Ifakara, United Republic of Tanzania
- Liverpool School of Tropical Medicine, Department of Vector Biology, Liverpool, United Kingdom
| | - Heather M Ferguson
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, United Kingdom.
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Camponovo F, Bever CA, Galactionova K, Smith T, Penny MA. Incidence and admission rates for severe malaria and their impact on mortality in Africa. Malar J 2017; 16:1. [PMID: 28049519 PMCID: PMC5209951 DOI: 10.1186/s12936-016-1650-6] [Citation(s) in RCA: 99] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 12/15/2016] [Indexed: 12/04/2022] Open
Abstract
Background Appropriate treatment of life-threatening Plasmodium falciparum malaria requires in-patient care. Although the proportion of severe cases accessing in-patient care in endemic settings strongly affects overall case fatality rates and thus disease burden, this proportion is generally unknown. At present, estimates of malaria mortality are driven by prevalence or overall clinical incidence data, ignoring differences in case fatality resulting from variations in access. Consequently, the overall impact of preventive interventions on disease burden have not been validly compared with those of improvements in access to case management or its quality. Methods Using a simulation-based approach, severe malaria admission rates and the subsequent severe malaria disease and mortality rates for 41 malaria endemic countries of sub-Saharan Africa were estimated. Country differences in transmission and health care settings were captured by use of high spatial resolution data on demographics and falciparum malaria prevalence, as well as national level estimates of effective coverage of treatment for uncomplicated malaria. Reported and modelled estimates of cases, admissions and malaria deaths from the World Malaria Report, along with predicted burden from simulations, were combined to provide revised estimates of access to in-patient care and case fatality rates. Results There is substantial variation between countries’ in-patient admission rates and estimated levels of case fatality rates. It was found that for many African countries, most patients admitted for in-patient treatment would not meet strict criteria for severe disease and that for some countries only a small proportion of the total severe cases are admitted. Estimates are highly sensitive to the assumed community case fatality rates. Re-estimation of national level malaria mortality rates suggests that there is substantial burden attributable to inefficient in-patient access and treatment of severe disease. Conclusions The model-based methods proposed here offer a standardized approach to estimate the numbers of severe malaria cases and deaths based on national level reporting, allowing for coverage of both curative and preventive interventions. This makes possible direct comparisons of the potential benefits of scaling-up either category of interventions. The profound uncertainties around these estimates highlight the need for better data. Electronic supplementary material The online version of this article (doi:10.1186/s12936-016-1650-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Flavia Camponovo
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Caitlin A Bever
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland.,Institute for Disease Modeling, Bellevue, WA, 98005, USA
| | - Katya Galactionova
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Thomas Smith
- Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Melissa A Penny
- Swiss Tropical and Public Health Institute, Basel, Switzerland. .,University of Basel, Basel, Switzerland.
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8
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Mapping a Knowledge-Based Malaria Hazard Index Related to Landscape Using Remote Sensing: Application to the Cross-Border Area between French Guiana and Brazil. REMOTE SENSING 2016. [DOI: 10.3390/rs8040319] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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9
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Howes RE, Reiner Jr. RC, Battle KE, Longbottom J, Mappin B, Ordanovich D, Tatem AJ, Drakeley C, Gething PW, Zimmerman PA, Smith DL, Hay SI. Plasmodium vivax Transmission in Africa. PLoS Negl Trop Dis 2015; 9:e0004222. [PMID: 26587988 PMCID: PMC4654493 DOI: 10.1371/journal.pntd.0004222] [Citation(s) in RCA: 81] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Accepted: 10/19/2015] [Indexed: 12/22/2022] Open
Abstract
Malaria in sub-Saharan Africa has historically been almost exclusively attributed to Plasmodium falciparum (Pf). Current diagnostic and surveillance systems in much of sub-Saharan Africa are not designed to identify or report non-Pf human malaria infections accurately, resulting in a dearth of routine epidemiological data about their significance. The high prevalence of Duffy negativity provided a rationale for excluding the possibility of Plasmodium vivax (Pv) transmission. However, review of varied evidence sources including traveller infections, community prevalence surveys, local clinical case reports, entomological and serological studies contradicts this viewpoint. Here, these data reports are weighted in a unified framework to reflect the strength of evidence of indigenous Pv transmission in terms of diagnostic specificity, size of individual reports and corroboration between evidence sources. Direct evidence was reported from 21 of the 47 malaria-endemic countries studied, while 42 countries were attributed with infections of visiting travellers. Overall, moderate to conclusive evidence of transmission was available from 18 countries, distributed across all parts of the continent. Approximately 86.6 million Duffy positive hosts were at risk of infection in Africa in 2015. Analysis of the mechanisms sustaining Pv transmission across this continent of low frequency of susceptible hosts found that reports of Pv prevalence were consistent with transmission being potentially limited to Duffy positive populations. Finally, reports of apparent Duffy-independent transmission are discussed. While Pv is evidently not a major malaria parasite across most of sub-Saharan Africa, the evidence presented here highlights its widespread low-level endemicity. An increased awareness of Pv as a potential malaria parasite, coupled with policy shifts towards species-specific diagnostics and reporting, will allow a robust assessment of the public health significance of Pv, as well as the other neglected non-Pf parasites, which are currently invisible to most public health authorities in Africa, but which can cause severe clinical illness and require specific control interventions.
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Affiliation(s)
- Rosalind E. Howes
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
- Center for Global Health and Diseases, Case Western Reserve University, Cleveland, Ohio, United States of America
- * E-mail:
| | - Robert C. Reiner Jr.
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, Indiana, United States of America
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Katherine E. Battle
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Joshua Longbottom
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Bonnie Mappin
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Dariya Ordanovich
- Department of Geography and Environment, University of Southampton, Highfield, Southampton, United Kingdom
| | - Andrew J. Tatem
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
- Department of Geography and Environment, University of Southampton, Highfield, Southampton, United Kingdom
- Flowminder Foundation, Stockholm, Sweden
| | - Chris Drakeley
- Malaria Centre, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Peter W. Gething
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Peter A. Zimmerman
- Center for Global Health and Diseases, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - David L. Smith
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
- Sanaria Institute for Global Health and Tropical Medicine, Rockville, Maryland, United States of America
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States of America
| | - Simon I. Hay
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States of America
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10
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Abstract
A malaria model is formulated which includes the enhanced attractiveness of infectious humans to mosquitoes, as result of host manipulation by malaria parasite, and the human behavior, represented by insecticide-treated bed-nets usage. The occurrence of a backward bifurcation at R0 = 1 is shown to be possible, which implies that multiple endemic equilibria co-exist with a stable disease-free equilibrium when the basic reproduction number is less than unity. This phenomenon is found to be caused by disease-induced human mortality. The global asymptotic stability of the endemic equilibrium for R0 > 1 is proved, by using the geometric method for global stability. Therefore, the disease becomes endemic for R0 > 1 regardless of the number of initial cases in both the human and vector populations. Finally, the impact on system dynamics of vector's host preferences and bed-net usage behavior is investigated.
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Affiliation(s)
- Bruno Buonomo
- Department of Mathematics and Applications, University of Naples Federico II, via Cintia, I-80126 Naples, Italy
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11
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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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12
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Heesterbeek H, Anderson RM, Andreasen V, Bansal S, De Angelis D, Dye C, Eames KTD, Edmunds WJ, Frost SDW, Funk S, Hollingsworth TD, House T, Isham V, Klepac P, Lessler J, Lloyd-Smith JO, Metcalf CJE, Mollison D, Pellis L, Pulliam JRC, Roberts MG, Viboud C. Modeling infectious disease dynamics in the complex landscape of global health. Science 2015; 347:aaa4339. [PMID: 25766240 PMCID: PMC4445966 DOI: 10.1126/science.aaa4339] [Citation(s) in RCA: 344] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health.
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Affiliation(s)
- Hans Heesterbeek
- Faculty of Veterinary Medicine, University of Utrecht, Utrecht, Netherlands.
| | | | | | | | | | | | - Ken T D Eames
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene Tropical Medicine, London, UK
| | - W John Edmunds
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene Tropical Medicine, London, UK
| | | | | | - T Deirdre Hollingsworth
- School of Life Sciences, University of Warwick, UK. School of Tropical Medicine, University of Liverpool, UK
| | - Thomas House
- Warwick Mathematics Institute, University of Warwick, Coventry, UK
| | - Valerie Isham
- Department of Statistical Science, University College London, London, UK
| | | | - Justin Lessler
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - James O Lloyd-Smith
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA
| | - C Jessica E Metcalf
- Department of Zoology, University of Oxford, Oxford, UK, and Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | | | - Lorenzo Pellis
- Warwick Mathematics Institute, University of Warwick, Coventry, UK
| | - Juliet R C Pulliam
- Department of Biology-Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA. Division of International Epidemiology and Population Studies, Fogarty International Center, NIH, Bethesda, MD, USA
| | - Mick G Roberts
- Institute of Natural and Mathematical Sciences, Massey University, Auckland, New Zealand
| | - Cecile Viboud
- Division of International Epidemiology and Population Studies, Fogarty International Center, NIH, Bethesda, MD, USA
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13
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Stuckey EM, Smith T, Chitnis N. Seasonally dependent relationships between indicators of malaria transmission and disease provided by mathematical model simulations. PLoS Comput Biol 2014; 10:e1003812. [PMID: 25187979 PMCID: PMC4154642 DOI: 10.1371/journal.pcbi.1003812] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2014] [Accepted: 07/17/2014] [Indexed: 11/24/2022] Open
Abstract
Evaluating the effectiveness of malaria control interventions on the basis of their impact on transmission as well as impact on morbidity and mortality is becoming increasingly important as countries consider pre-elimination and elimination as well as disease control. Data on prevalence and transmission are traditionally obtained through resource-intensive epidemiological and entomological surveys that become difficult as transmission decreases. This work employs mathematical modeling to examine the relationships between malaria indicators allowing more easily measured data, such as routine health systems data on case incidence, to be translated into measures of transmission and other malaria indicators. Simulations of scenarios with different levels of malaria transmission, patterns of seasonality and access to treatment were run with an ensemble of models of malaria epidemiology and within-host dynamics, as part of the OpenMalaria modeling platform. For a given seasonality profile, regression analysis mapped simulation results of malaria indicators, such as annual average entomological inoculation rate, prevalence, incidence of uncomplicated and severe episodes, and mortality, to an expected range of values of any of the other indicators. Results were validated by comparing simulated relationships between indicators with previously published data on these same indicators as observed in malaria endemic areas. These results allow for direct comparisons of malaria transmission intensity estimates made using data collected with different methods on different indicators. They also address key concerns with traditional methods of quantifying transmission in areas of differing transmission intensity and sparse data. Although seasonality of transmission is often ignored in data compilations, the models suggest it can be critically important in determining the relationship between transmission and disease. Application of these models could help public health officials detect changes of disease dynamics in a population and plan and assess the impact of malaria control interventions. While malaria is still a major public health problem in many parts of the world, control programs have greatly reduced the burden of disease in recent years and many countries are now considering the goal of elimination. Unfortunately, malaria transmission becomes more difficult to measure when it is low because traditional methods involve capturing mosquitoes; an expensive and time-consuming technique. To measure transmission in areas without adequate field data, we run simulations of a mathematical model of malaria over a range of transmission intensities and seasonal patterns to examine how different measurements of malaria (prevalence, clinical disease, and death) relate to each other, how they relate to transmission, and if the relationships are likely to vary by seasonal pattern of transmission. These simulated relationships allow us to translate easily measured data, such as clinical case incidence seen at health facilities, into estimates of transmission. This technique can help public health officials plan and assess the impact of malaria control interventions, even in areas without intensive research activities.
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Affiliation(s)
- Erin M. Stuckey
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
- * E-mail:
| | - Thomas 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
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