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Baldoquín Rodríguez W, Mirabal M, Van der Stuyft P, Gómez Padrón T, Fonseca V, Castillo RM, Monteagudo Díaz S, Baetens JM, De Baets B, Toledo Romaní ME, Vanlerberghe V. The Potential of Surveillance Data for Dengue Risk Mapping: An Evaluation of Different Approaches in Cuba. Trop Med Infect Dis 2023; 8:tropicalmed8040230. [PMID: 37104355 PMCID: PMC10143650 DOI: 10.3390/tropicalmed8040230] [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: 02/13/2023] [Revised: 04/03/2023] [Accepted: 04/11/2023] [Indexed: 04/28/2023] Open
Abstract
To better guide dengue prevention and control efforts, the use of routinely collected data to develop risk maps is proposed. For this purpose, dengue experts identified indicators representative of entomological, epidemiological and demographic risks, hereafter called components, by using surveillance data aggregated at the level of Consejos Populares (CPs) in two municipalities of Cuba (Santiago de Cuba and Cienfuegos) in the period of 2010-2015. Two vulnerability models (one with equally weighted components and one with data-derived weights using Principal Component Analysis), and three incidence-based risk models were built to construct risk maps. The correlation between the two vulnerability models was high (tau > 0.89). The single-component and multicomponent incidence-based models were also highly correlated (tau ≥ 0.9). However, the agreement between the vulnerability- and the incidence-based risk maps was below 0.6 in the setting with a prolonged history of dengue transmission. This may suggest that an incidence-based approach does not fully reflect the complexity of vulnerability for future transmission. The small difference between single- and multicomponent incidence maps indicates that in a setting with a narrow availability of data, simpler models can be used. Nevertheless, the generalized linear mixed multicomponent model provides information of covariate-adjusted and spatially smoothed relative risks of disease transmission, which can be important for the prospective evaluation of an intervention strategy. In conclusion, caution is needed when interpreting risk maps, as the results vary depending on the importance given to the components involved in disease transmission. The multicomponent vulnerability mapping needs to be prospectively validated based on an intervention trial targeting high-risk areas.
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Affiliation(s)
| | - Mayelin Mirabal
- Unidad de Información y Biblioteca, Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
| | | | - Tania Gómez Padrón
- Centro Provincial de Higiene Epidemiología y Microbiología, Dirección Provincial de Salud, Santiago de Cuba 90100, Cuba
| | - Viviana Fonseca
- Centro Provincial de Higiene Epidemiología y Microbiología, Dirección Provincial de Salud, Santiago de Cuba 90100, Cuba
| | - Rosa María Castillo
- Unidad Provincial de Vigilancia y Lucha Antivectorial, Dirección Provincial de Salud, Santiago de Cuba 90100, Cuba
| | - Sonia Monteagudo Díaz
- Centro Provincial de Higiene Epidemiología y Microbiología, Dirección Provincial de Salud, Cienfuegos 55100, Cuba
| | - Jan M Baetens
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
| | - Bernard De Baets
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
| | | | - Veerle Vanlerberghe
- Public Health Department, Institute of Tropical Medicine, Nationalestraat 155, 2000 Antwerp, Belgium
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2
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Fornace KM, Senyonjo L, Martin DL, Gwyn S, Schmidt E, Agyemang D, Marfo B, Addy J, Mensah E, Solomon AW, Bailey R, Drakeley CJ, Pullan RL. Characterising spatial patterns of neglected tropical disease transmission using integrated sero-surveillance in Northern Ghana. PLoS Negl Trop Dis 2022; 16:e0010227. [PMID: 35259153 PMCID: PMC8932554 DOI: 10.1371/journal.pntd.0010227] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/18/2022] [Accepted: 02/03/2022] [Indexed: 11/18/2022] Open
Abstract
Background
As prevalence decreases in pre-elimination settings, identifying the spatial distribution of remaining infections to target control measures becomes increasingly challenging. By measuring multiple antibody responses indicative of past exposure to different pathogens, integrated serological surveys enable simultaneous characterisation of residual transmission of multiple pathogens.
Methodology/Principal findings
Here, we combine integrated serological surveys with geostatistical modelling and remote sensing-derived environmental data to estimate the spatial distribution of exposure to multiple diseases in children in Northern Ghana. The study utilised the trachoma surveillance survey platform (cross-sectional two-stage cluster-sampled surveys) to collect information on additional identified diseases at different stages of elimination with minimal additional cost. Geostatistical modelling of serological data allowed identification of areas with high probabilities of recent exposure to diseases of interest, including areas previously unknown to control programmes. We additionally demonstrate how serological surveys can be used to identify areas with exposure to multiple diseases and to prioritise areas with high uncertainty for future surveys. Modelled estimates of cluster-level prevalence were strongly correlated with more operationally feasible metrics of antibody responses.
Conclusions/Significance
This study demonstrates the potential of integrated serological surveillance to characterise spatial distributions of exposure to multiple pathogens in low transmission and elimination settings when the probability of detecting infections is low.
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Affiliation(s)
- Kimberly M. Fornace
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow, United Kingdom
- * E-mail:
| | - Laura Senyonjo
- Research Team, Sightsavers UK, Haywards Heath, United Kingdom
| | - Diana L. Martin
- Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Sarah Gwyn
- Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Elena Schmidt
- Research Team, Sightsavers UK, Haywards Heath, United Kingdom
| | | | - Benjamin Marfo
- Neglected Tropical Disease Team, Ghana Health Service, Accra, Ghana
| | - James Addy
- Neglected Tropical Disease Team, Ghana Health Service, Accra, Ghana
| | | | - Anthony W. Solomon
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Department of Control of Neglected Tropical Diseases, World Health Organization, Geneva, Switzerland
| | - Robin Bailey
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Chris J. Drakeley
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Rachel L. Pullan
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
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3
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Kyomuhangi I, Giorgi E. A threshold-free approach with age-dependency for estimating malaria seroprevalence. Malar J 2022; 21:1. [PMID: 34980109 PMCID: PMC8725324 DOI: 10.1186/s12936-021-04022-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 12/12/2021] [Indexed: 11/21/2022] Open
Abstract
Background In malaria serology analysis, the standard approach to obtain seroprevalence, i.e the proportion of seropositive individuals in a population, is based on a threshold which is used to classify individuals as seropositive or seronegative. The choice of this threshold is often arbitrary and is based on methods that ignore the age-dependency of the antibody distribution. Methods Using cross-sectional antibody data from the Western Kenyan Highlands, this paper introduces a novel approach that has three main advantages over the current threshold-based approach: it avoids the use of thresholds; it accounts for the age dependency of malaria antibodies; and it allows us to propagate the uncertainty from the classification of individuals into seropositive and seronegative when estimating seroprevalence. The reversible catalytic model is used as an example for illustrating how to propagate this uncertainty into the parameter estimates of the model. Results This paper finds that accounting for age-dependency leads to a better fit to the data than the standard approach which uses a single threshold across all ages. Additionally, the paper also finds that the proposed threshold-free approach is more robust against the selection of different age-groups when estimating seroprevalence. Conclusion The novel threshold-free approach presented in this paper provides a statistically principled and more objective approach to estimating malaria seroprevalence. The introduced statistical framework also provides a means to compare results across studies which may use different age ranges for the estimation of seroprevalence. Supplementary Information The online version contains supplementary material available at 10.1186/s12936-021-04022-4.
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Affiliation(s)
- Irene Kyomuhangi
- CHICAS, Lancaster University, Sir John Fisher Drive, Lancaster, UK.
| | - Emanuele Giorgi
- CHICAS, Lancaster University, Sir John Fisher Drive, Lancaster, UK
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4
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Spatial cluster analysis of Plasmodium vivax and P. malariae exposure using serological data among Haitian school children sampled between 2014 and 2016. PLoS Negl Trop Dis 2022; 16:e0010049. [PMID: 34986142 PMCID: PMC8765618 DOI: 10.1371/journal.pntd.0010049] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 01/18/2022] [Accepted: 12/03/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Estimation of malaria prevalence in very low transmission settings is difficult by even the most advanced diagnostic tests. Antibodies against malaria antigens provide an indicator of active or past exposure to these parasites. The prominent malaria species within Haiti is Plasmodium falciparum, but P. vivax and P. malariae infections are also known to be endemic. METHODOLOGY/PRINCIPAL FINDINGS From 2014-2016, 28,681 Haitian children were enrolled in school-based serosurveys and were asked to provide a blood sample for detection of antibodies against multiple infectious diseases. IgG against the P. falciparum, P. vivax, and P. malariae merozoite surface protein 19kD subunit (MSP119) antigens was detected by a multiplex bead assay (MBA). A subset of samples was also tested for Plasmodium DNA by PCR assays, and for Plasmodium antigens by a multiplex antigen detection assay. Geospatial clustering of high seroprevalence areas for P. vivax and P. malariae antigens was assessed by both Ripley's K-function and Kulldorff's spatial scan statistic. Of 21,719 children enrolled in 680 schools in Haiti who provided samples to assay for IgG against PmMSP119, 278 (1.27%) were seropositive. Of 24,559 children enrolled in 788 schools providing samples for PvMSP119 serology, 113 (0.46%) were seropositive. Two significant clusters of seropositivity were identified throughout the country for P. malariae exposure, and two identified for P. vivax. No samples were found to be positive for Plasmodium DNA or antigens. CONCLUSIONS/SIGNIFICANCE From school-based surveys conducted from 2014 to 2016, very few Haitian children had evidence of exposure to P. vivax or P. malariae, with no children testing positive for active infection. Spatial scan statistics identified non-overlapping areas of the country with higher seroprevalence for these two malarias. Serological data provides useful information of exposure to very low endemic malaria species in a population that is unlikely to present to clinics with symptomatic infections.
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5
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Amoah B, McCann RS, Kabaghe AN, Mburu M, Chipeta MG, Moraga P, Gowelo S, Tizifa T, van den Berg H, Mzilahowa T, Takken W, van Vugt M, Phiri KS, Diggle PJ, Terlouw DJ, Giorgi E. Identifying Plasmodium falciparum transmission patterns through parasite prevalence and entomological inoculation rate. eLife 2021; 10:65682. [PMID: 34672946 PMCID: PMC8530514 DOI: 10.7554/elife.65682] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 09/30/2021] [Indexed: 11/13/2022] Open
Abstract
Background Monitoring malaria transmission is a critical component of efforts to achieve targets for elimination and eradication. Two commonly monitored metrics of transmission intensity are parasite prevalence (PR) and the entomological inoculation rate (EIR). Comparing the spatial and temporal variations in the PR and EIR of a given geographical region and modelling the relationship between the two metrics may provide a fuller picture of the malaria epidemiology of the region to inform control activities. Methods Using geostatistical methods, we compare the spatial and temporal patterns of Plasmodium falciparum EIR and PR using data collected over 38 months in a rural area of Malawi. We then quantify the relationship between EIR and PR by using empirical and mechanistic statistical models. Results Hotspots identified through the EIR and PR partly overlapped during high transmission seasons but not during low transmission seasons. The estimated relationship showed a 1-month delayed effect of EIR on PR such that at lower levels of EIR, increases in EIR are associated with rapid rise in PR, whereas at higher levels of EIR, changes in EIR do not translate into notable changes in PR. Conclusions Our study emphasises the need for integrated malaria control strategies that combine vector and human host managements monitored by both entomological and parasitaemia indices. Funding This work was supported by Stichting Dioraphte grant number 13050800.
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Affiliation(s)
- Benjamin Amoah
- Centre for Health Informatics, Computing, and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - Robert S McCann
- Laboratory of Entomology, Wageningen University and Research, Wageningen, Netherlands.,Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi.,Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, United States
| | - Alinune N Kabaghe
- Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi.,Academic Medical Centre, University of Amsterdam, Amsterdam, Netherlands
| | - Monicah Mburu
- Laboratory of Entomology, Wageningen University and Research, Wageningen, Netherlands.,Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi
| | - Michael G Chipeta
- Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi.,Malawi-Liverpool Wellcome Trust Research Programme, Blantyre, Malawi.,Big Data Institute, University of Oxford, Oxford, United Kingdom
| | - Paula Moraga
- Centre for Health Informatics, Computing, and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster, United Kingdom.,Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Steven Gowelo
- Laboratory of Entomology, Wageningen University and Research, Wageningen, Netherlands.,Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi
| | - Tinashe Tizifa
- Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi.,Academic Medical Centre, University of Amsterdam, Amsterdam, Netherlands
| | - Henk van den Berg
- Laboratory of Entomology, Wageningen University and Research, Wageningen, Netherlands
| | - Themba Mzilahowa
- Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi
| | - Willem Takken
- Laboratory of Entomology, Wageningen University and Research, Wageningen, Netherlands
| | - Michele van Vugt
- Academic Medical Centre, University of Amsterdam, Amsterdam, Netherlands
| | - Kamija S Phiri
- Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi
| | - Peter J Diggle
- Centre for Health Informatics, Computing, and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - Dianne J Terlouw
- Department of Public Health, College of Medicine, University of Malawi, Blantyre, Malawi.,Malawi-Liverpool Wellcome Trust Research Programme, Blantyre, Malawi.,Liverpool School of Tropical Medicine, Liverpool, United Kingdom
| | - Emanuele Giorgi
- Centre for Health Informatics, Computing, and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
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6
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Abstract
Serology data are an increasingly important tool in malaria surveillance, especially in low transmission settings where the estimation of parasite-based indicators is often problematic. Existing methods rely on the use of thresholds to identify seropositive individuals and estimate transmission intensity, while making assumptions about the temporal dynamics of malaria transmission that are rarely questioned. Here, we present a novel threshold-free approach for the analysis of malaria serology data which avoids dichotomization of continuous antibody measurements and allows us to model changes in the antibody distribution across age in a more flexible way. The proposed unified mechanistic model combines the properties of reversible catalytic and antibody acquisition models, and allows for temporally varying boosting and seroconversion rates. Additionally, as an alternative to the unified mechanistic model, we also propose an empirical approach to analysis where modelling of the age-dependency is informed by the data rather than biological assumptions. Using serology data from Western Kenya, we demonstrate both the usefulness and limitations of the novel modelling framework.
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7
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A Model for Assessing the Quantitative Effects of Heterogeneous Affinity in Malaria Transmission along with Ivermectin Mass Administration. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10238696] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Using an agent-based model of malaria, we present numerical evidence that in communities of individuals having an affinity varying within a broad range of values, disease transmission may increase up to 300%. Moreover, our findings provide new insight into how to combine different strategies for the prevention of malaria transmission. In particular, we uncover a relationship between the level of heterogeneity and the level of conventional and unconventional anti-malarial drug administration (ivermectin and gametocidal agents), which, when taken together, will define a control parameter, tuning between disease persistence and elimination. Finally, we also provide evidence that the entomological inoculation rate, as well as the product between parasite and sporozoite rates are both good indicators of malaria incidence in the presence of heterogeneity in disease transmission and may configure a possible improvement in that setting, upon classical standard measures such as the basic reproductive number.
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8
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Kittur N, Campbell CH, Binder S, Shen Y, Wiegand RE, Mwanga JR, Kinung'hi SM, Musuva RM, Odiere MR, Matendechero SH, Knopp S, Colley DG. Discovering, Defining, and Summarizing Persistent Hotspots in SCORE Studies. Am J Trop Med Hyg 2020; 103:24-29. [PMID: 32400365 PMCID: PMC7351310 DOI: 10.4269/ajtmh.19-0815] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The Schistosomiasis Consortium for Operational Research and Evaluation (SCORE) conducted large field studies on schistosomiasis control and elimination in Africa. All of these studies, carried out in low-, moderate-, and high-prevalence areas, resulted in a reduction in prevalence and intensity of Schistosoma infection after repeated mass drug administration (MDA). However, in all studies, there were locations that experienced minimal or no decline or even increased in prevalence and/or intensity. These areas are termed persistent hotspots (PHS). In SCORE studies in medium- to high-prevalence areas, at least 30% of study villages were PHS. There was no consistent relationship between PHS and the type or frequency of intervention, adequacy of reported MDA coverage, and prevalence or intensity of infection at baseline. In a series of small studies, factors that differed between PHS and villages that responded to repeated MDA as expected included sources of water for personal use, sanitation, and hygiene. SCORE studies comparing PHS with villages that responded to MDA suggest the potential for PHS to be identified after a few years of MDA. However, additional studies in different social-ecological settings are needed to develop generalizable approaches that program managers can use to identify and address PHS. This is essential if goals for schistosomiasis control and elimination are to be achieved.
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Affiliation(s)
- Nupur Kittur
- Schistosomiasis Consortium for Operational Research and Evaluation, Center for Tropical and Emerging Global Diseases, University of Georgia, Athens, Georgia
| | - Carl H Campbell
- Schistosomiasis Consortium for Operational Research and Evaluation, Center for Tropical and Emerging Global Diseases, University of Georgia, Athens, Georgia
| | - Sue Binder
- Schistosomiasis Consortium for Operational Research and Evaluation, Center for Tropical and Emerging Global Diseases, University of Georgia, Athens, Georgia
| | - Ye Shen
- Department of Epidemiology & Biostatistics, University of Georgia, Athens, Georgia
| | - Ryan E Wiegand
- University of Basel, Basel, Switzerland.,Swiss Tropical and Public Health Institute, Basel, Switzerland.,Division of Parasitic Diseases and Malaria, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Joseph R Mwanga
- Department of Epidemiology, Biostatistics and Behavioral Sciences, School of Public Health, Catholic University of Health and Allied Sciences, Mwanza, Tanzania
| | - Safari M Kinung'hi
- Mwanza Research Centre, National Institute of Medical Research, Mwanza, Tanzania
| | - Rosemary M Musuva
- Neglected Tropical Diseases Unit, Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Maurice R Odiere
- Neglected Tropical Diseases Unit, Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Sultani H Matendechero
- Division of Vector Borne and Neglected Tropical Diseases, Ministry of Health, Nairobi, Kenya
| | - Stefanie Knopp
- University of Basel, Basel, Switzerland.,Swiss Tropical and Public Health Institute, Basel, Switzerland
| | - Daniel G Colley
- Department of Microbiology, University of Georgia, Athens, Georgia.,Schistosomiasis Consortium for Operational Research and Evaluation, Center for Tropical and Emerging Global Diseases, University of Georgia, Athens, Georgia
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9
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Surendra H, Supargiyono, Ahmad RA, Kusumasari RA, Rahayujati TB, Damayanti SY, Tetteh KKA, Chitnis C, Stresman G, Cook J, Drakeley C. Using health facility-based serological surveillance to predict receptive areas at risk of malaria outbreaks in elimination areas. BMC Med 2020; 18:9. [PMID: 31987052 PMCID: PMC6986103 DOI: 10.1186/s12916-019-1482-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 12/09/2019] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND In order to improve malaria burden estimates in low transmission settings, more sensitive tools and efficient sampling strategies are required. This study evaluated the use of serological measures from repeated health facility-based cross-sectional surveys to investigate Plasmodium falciparum and Plasmodium vivax transmission dynamics in an area nearing elimination in Indonesia. METHODS Quarterly surveys were conducted in eight public health facilities in Kulon Progo District, Indonesia, from May 2017 to April 2018. Demographic data were collected from all clinic patients and their companions, with household coordinates collected using participatory mapping methods. In addition to standard microscopy tests, bead-based serological assays were performed on finger-prick bloodspot samples from 9453 people. Seroconversion rates (SCR, i.e. the proportion of people in the population who are expected to seroconvert per year) were estimated by fitting a simple reversible catalytic model to seroprevalence data. Mixed effects logistic regression was used to examine factors associated with malaria exposure, and spatial analysis was performed to identify areas with clustering of high antibody responses. RESULTS Parasite prevalence by microscopy was extremely low (0.06% (95% confidence interval 0.03-0.14, n = 6) and 0 for P. vivax and P. falciparum, respectively). However, spatial analysis of P. vivax antibody responses identified high-risk areas that were subsequently the site of a P. vivax outbreak in August 2017 (62 cases detected through passive and reactive detection systems). These areas overlapped with P. falciparum high-risk areas and were detected in each survey. General low transmission was confirmed by the SCR estimated from a pool of the four surveys in people aged 15 years old and under (0.020 (95% confidence interval 0.017-0.024) and 0.005 (95% confidence interval 0.003-0.008) for P. vivax and P. falciparum, respectively). The SCR estimates in those over 15 years old were 0.066 (95% confidence interval 0.041-0.105) and 0.032 (95% confidence interval 0.015-0.069) for P. vivax and P. falciparum, respectively. CONCLUSIONS These findings demonstrate the potential use of health facility-based serological surveillance to better identify and target areas still receptive to malaria in an elimination setting. Further implementation research is needed to enable integration of these methods with existing surveillance systems.
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Affiliation(s)
- Henry Surendra
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT UK
- Centre for Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jl. Medika, Yogyakarta, 55281 Indonesia
| | - Supargiyono
- Centre for Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jl. Medika, Yogyakarta, 55281 Indonesia
- Department of Parasitology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Sekip Utara, Yogyakarta, 55281 Indonesia
| | - Riris A. Ahmad
- Centre for Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jl. Medika, Yogyakarta, 55281 Indonesia
- Department of Biostatistics, Epidemiology and Population Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Sekip Utara, Yogyakarta, 55281 Indonesia
| | - Rizqiani A. Kusumasari
- Centre for Tropical Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Jl. Medika, Yogyakarta, 55281 Indonesia
- Department of Parasitology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Sekip Utara, Yogyakarta, 55281 Indonesia
| | | | - Siska Y. Damayanti
- District Health Office of Kulon Progo, Jln. Suparman No 1, Wates, 55611 Indonesia
| | - Kevin K. A. Tetteh
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT UK
| | | | - Gillian Stresman
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT UK
| | - Jackie Cook
- MRC Tropical Epidemiology Group, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT UK
| | - Chris Drakeley
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT UK
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10
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Stresman G, Bousema T, Cook J. Malaria Hotspots: Is There Epidemiological Evidence for Fine-Scale Spatial Targeting of Interventions? Trends Parasitol 2019; 35:822-834. [PMID: 31474558 DOI: 10.1016/j.pt.2019.07.013] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 07/29/2019] [Accepted: 07/29/2019] [Indexed: 12/20/2022]
Abstract
As data at progressively granular spatial scales become available, the temptation is to target interventions to areas with higher malaria transmission - so-called hotspots - with the aim of reducing transmission in the wider community. This paper reviews literature to determine if hotspots are an intrinsic feature of malaria epidemiology and whether current evidence supports hotspot-targeted interventions. Hotspots are a consistent feature of malaria transmission at all endemicities. The smallest spatial unit capable of supporting transmission is the household, where peri-domestic transmission occurs. Whilst the value of focusing interventions to high-burden areas is evident, there is currently limited evidence that local-scale hotspots fuel transmission. As boundaries are often uncertain, there is no conclusive evidence that hotspot-targeted interventions accelerate malaria elimination.
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Affiliation(s)
- Gillian Stresman
- Infection Biology Department, London School of Hygiene and Tropical Medicine, London, UK.
| | - Teun Bousema
- Radboud University Medical Centre, Department of Microbiology, HB Nijmegen, The Netherlands.
| | - Jackie Cook
- Medical Research Council (MRC) Tropical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK
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11
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Kang SY, Battle KE, Gibson HS, Cooper LV, Maxwell K, Kamya M, Lindsay SW, Dorsey G, Greenhouse B, Rodriguez-Barraquer I, Reiner RCJ, Smith DL, Bisanzio D. Heterogeneous exposure and hotspots for malaria vectors at three study sites in Uganda. Gates Open Res 2018; 2:32. [PMID: 30706054 PMCID: PMC6350504 DOI: 10.12688/gatesopenres.12838.2] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/01/2018] [Indexed: 11/30/2022] Open
Abstract
Background: Heterogeneity in malaria transmission has household, temporal, and spatial components. These factors are relevant for improving the efficiency of malaria control by targeting heterogeneity. To quantify variation, we analyzed mosquito counts from entomological surveillance conducted at three study sites in Uganda that varied in malaria transmission intensity. Mosquito biting or exposure is a risk factor for malaria transmission. Methods: Using a Bayesian zero-inflated negative binomial model, validated via a comprehensive simulation study, we quantified household differences in malaria vector density and examined its spatial distribution. We introduced a novel approach for identifying changes in vector abundance hotspots over time by computing the Getis-Ord statistic on ratios of household biting propensities for different scenarios. We also explored the association of household biting propensities with housing and environmental covariates. Results: In each site, there was evidence for hot and cold spots of vector abundance, and spatial patterns associated with urbanicity, elevation, or other environmental covariates. We found some differences in the hotspots in rainy vs. dry seasons or before vs. after the application of control interventions. Housing quality explained a portion of the variation among households in mosquito counts. Conclusion: This work provided an improved understanding of heterogeneity in malaria vector density at the three study sites in Uganda and offered a valuable opportunity for assessing whether interventions could be spatially targeted to be aimed at abundance hotspots which may increase malaria risk. Indoor residual spraying was shown to be a successful measure of vector control interventions in Tororo, Uganda. Cement walls, brick floors, closed eaves, screened airbricks, and tiled roofs were features of a house that had shown reduction of household biting propensity. Improvements in house quality should be recommended as a supplementary measure for malaria control reducing risk of infection.
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Affiliation(s)
- Su Yun Kang
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Katherine E Battle
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Harry S Gibson
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Laura V Cooper
- Department of Veterinary Medicine, Cambridge University, Cambridge, UK
| | - Kilama Maxwell
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Moses Kamya
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | | | - Grant Dorsey
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Bryan Greenhouse
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | | | - Robert C Jr Reiner
- Institute for Health Metrics & Evaluation, University of Washington, Seattle, WA, USA
| | - David L Smith
- Institute for Health Metrics & Evaluation, University of Washington, Seattle, WA, USA
| | - Donal Bisanzio
- RTI International, Washington DC, USA.,Centre for Tropical Diseases, Sacro Cuore-Don Calabria Hospital, Negrar, Italy
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12
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Stresman GH, Mwesigwa J, Achan J, Giorgi E, Worwui A, Jawara M, Di Tanna GL, Bousema T, Van Geertruyden JP, Drakeley C, D'Alessandro U. Do hotspots fuel malaria transmission: a village-scale spatio-temporal analysis of a 2-year cohort study in The Gambia. BMC Med 2018; 16:160. [PMID: 30213275 PMCID: PMC6137946 DOI: 10.1186/s12916-018-1141-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Accepted: 07/31/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Despite the biological plausibility of hotspots fueling malaria transmission, the evidence to support this concept has been mixed. If transmission spreads from high burden to low burden households in a consistent manner, then this could have important implications for control and elimination program development. METHODS Data from a longitudinal cohort in The Gambia was analyzed. All consenting individuals residing in 12 villages across the country were sampled monthly from June (dry season) to December 2013 (wet season), in April 2014 (mid dry season), and monthly from June to December 2014. A study nurse stationed within each village recorded passively detected malaria episodes between visits. Plasmodium falciparum infections were determined by polymerase chain reaction and analyzed using a geostatistical model. RESULTS Household-level observed monthly incidence ranged from 0 to 0.50 infection per person (interquartile range = 0.02-0.10) across the sampling months, and high burden households exist across all study villages. There was limited evidence of a spatio-temporal pattern at the monthly timescale irrespective of transmission intensity. Within-household transmission was the most plausible hypothesis examined to explain the observed heterogeneity in infections. CONCLUSIONS Within-village malaria transmission patterns are concentrated in a small proportion of high burden households, but patterns are stochastic regardless of endemicity. Our findings support the notion of transmission occurring at the household and village scales but not the use of a targeted approach to interrupt spreading of infections from high to low burden areas within villages in this setting.
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Affiliation(s)
- Gillian H Stresman
- Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, UK.
| | - Julia Mwesigwa
- Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine, Fajara, The Gambia.,University of Antwerp, Antwerp, Belgium
| | - Jane Achan
- Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine, Fajara, The Gambia.,University of Antwerp, Antwerp, Belgium
| | - Emanuele Giorgi
- CHICAS, Lancaster Medical School, Lancaster University, Lancaster, UK
| | - Archibald Worwui
- Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine, Fajara, The Gambia.,University of Antwerp, Antwerp, Belgium
| | - Musa Jawara
- Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine, Fajara, The Gambia.,University of Antwerp, Antwerp, Belgium
| | | | - Teun Bousema
- Department of Medical Microbology, Radboud Medical University, Nijmegen, The Netherlands
| | | | - Chris Drakeley
- Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, UK
| | - Umberto D'Alessandro
- Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, UK.,Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine, Fajara, The Gambia.,University of Antwerp, Antwerp, Belgium
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13
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Micro-epidemiology of mixed-species malaria infections in a rural population living in the Colombian Amazon region. Sci Rep 2018; 8:5543. [PMID: 29615693 PMCID: PMC5883018 DOI: 10.1038/s41598-018-23801-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 03/21/2018] [Indexed: 12/19/2022] Open
Abstract
Malaria outbreaks have been reported in recent years in the Colombian Amazon region, malaria has been re-emerging in areas where it was previously controlled. Information from malaria transmission networks and knowledge about the population characteristics influencing the dispersal of parasite species is limited. This study aimed to determine the distribution patterns of Plasmodium vivax, P. malariae and P. falciparum single and mixed infections, as well as the significant socio-spatial groupings relating to the appearance of such infections. An active search in 57 localities resulted in 2,106 symptomatic patients being enrolled. Parasitaemia levels were assessed by optical microscopy, and parasites were detected by PCR. The association between mixed infections (in 43.2% of the population) and socio-spatial factors was modelled using logistic regression and multiple correspondence analyses. P. vivax occurred most frequently (71.0%), followed by P. malariae (43.2%), in all localities. The results suggest that a parasite density-dependent regulation model (with fever playing a central role) was appropriate for modelling the frequency of mixed species infections in this population. This study highlights the under-reporting of Plasmodium spp. mixed infections in the malaria-endemic area of the Colombian Amazon region and the association between causative and environmental factors in such areas.
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14
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Giorgi E, Osman AA, Hassan AH, Ali AA, Ibrahim F, Amran JGH, Noor AM, Snow RW. Using non-exceedance probabilities of policy-relevant malaria prevalence thresholds to identify areas of low transmission in Somalia. Malar J 2018; 17:88. [PMID: 29463264 PMCID: PMC5819647 DOI: 10.1186/s12936-018-2238-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Accepted: 02/15/2018] [Indexed: 11/16/2022] Open
Abstract
Background Countries planning malaria elimination must adapt from sustaining universal control to targeted intervention and surveillance. Decisions to make this transition require interpretable information, including malaria parasite survey data. As transmission declines, observed parasite prevalence becomes highly heterogeneous with most communities reporting estimates close to zero. Absolute estimates of prevalence become hard to interpret as a measure of transmission intensity and suitable statistical methods are required to handle uncertainty of area-wide predictions that are programmatically relevant. Methods A spatio-temporal geostatistical binomial model for Plasmodium falciparum prevalence (PfPR) was developed using data from cross-sectional surveys conducted in Somalia in 2005, 2007–2011 and 2014. The fitted model was then used to generate maps of non-exceedance probabilities, i.e. the predictive probability that the region-wide population-weighted average PfPR for children between 2 and 10 years (PfPR2–10) lies below 1 and 5%. A comparison was carried out with the decision-making outcomes from those of standard approaches that ignore uncertainty in prevalence estimates. Results By 2010, most regions in Somalia were at least 70% likely to be below 5% PfPR2–10 and, by 2014, 17 regions were below 5% PfPR2–10 with a probability greater than 90%. Larger uncertainty is observed using a threshold of 1%. By 2011, only two regions were more than 90% likely of being < 1% PfPR2–10 and, by 2014, only three regions showed such low level of uncertainty. The use of non-exceedance probabilities indicated that there was weak evidence to classify 10 out of the 18 regions as < 1% in 2014, when a greater than 90% non-exceedance probability was required. Conclusion Unlike standard approaches, non-exceedance probabilities of spatially modelled PfPR2–10 allow to quantify uncertainty of prevalence estimates in relation to policy relevant intervention thresholds, providing programmatically relevant metrics to make decisions on transitioning from sustained malaria control to strategies that encompass methods of malaria elimination. Electronic supplementary material The online version of this article (10.1186/s12936-018-2238-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Emanuele Giorgi
- Lancaster Medical School, Lancaster University, Lancaster, UK.
| | | | | | | | | | | | - Abdisalan M Noor
- Population and Health Theme, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Robert W Snow
- Population and Health Theme, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
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