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Inference of malaria reproduction numbers in three elimination settings by combining temporal data and distance metrics.

ABSTRACT: Individual-level geographic information about malaria cases, such as the GPS coordinates of residence or health facility, is often collected as part of surveillance in near-elimination settings, but could be more effectively utilised to infer transmission dynamics, in conjunction with additional information such as symptom onset time and genetic distance. However, in the absence of data about the flow of parasites between populations, the spatial scale of malaria transmission is often not clear. As a result, it is important to understand the impact of varying assumptions about the spatial scale of transmission on key metrics of malaria transmission, such as reproduction numbers. We developed a method which allows the flexible integration of distance metrics (such as Euclidian distance, genetic distance or accessibility matrices) with temporal information into a single inference framework to infer malaria reproduction numbers. Twelve scenarios were defined, representing different assumptions about the likelihood of transmission occurring over different geographic distances and likelihood of missing infections (as well as high and low amounts of uncertainty in this estimate). These scenarios were applied to four individual level datasets from malaria eliminating contexts to estimate individual reproduction numbers and how they varied over space and time. Model comparison suggested that including spatial information improved models as measured by second order AIC (ΔAICc), compared to time only results. Across scenarios and across datasets, including spatial information tended to increase the seasonality of temporal patterns in reproduction numbers and reduced noise in the temporal distribution of reproduction numbers. The best performing parameterisations assumed long-range transmission (> 200 km) was possible. Our approach is flexible and provides the potential to incorporate other sources of information which can be converted into distance or adjacency matrices such as travel times or molecular markers.

SUBMITTER: Routledge I

PROVIDER: S-EPMC8280212 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: In malaria endemic areas, continuous exposure to Plasmodium parasites leads to asymptomatic carriers that provide a fundamental reservoir of parasites, contributing to the persistence of malaria transmission. Therefore, in the present investigation, the presence and prevalence of malaria asymptomatic cases were determined to evaluate the reservoir of infection in two malaria endemic areas with a previous history of malaria transmission in the south of Iran, Bashagard and Ghale-Ganj districts of Hormozgan and Kerman provinces, respectively, where malaria transmission has been drastically reduced in the recent years. METHODS: The population samples (n = 500 from each of the studied areas) were randomly collected from non-febrile, long-term residing, aged two to over 60 years, during 20092010. Three identical surveys were carried out in both study areas and in each phase all the consent participants were interviewed and clinically examined. In all, three surveys to detect hidden parasite reservoirs (both Plasmodium falciparum and Plasmodium vivax), thick and thin blood smears and a highly sensitive nested-PCR were applied. In addition, the sero-prevalence survey for detecting malaria exposure was done by using a serological marker. RESULTS: In this study, P. vivax and P. falciparum parasites were not detected by light microscopy and nested-PCR assay in all three surveys of samples. Antibody responses against P. vivax and P. falciparum were detected in 1% and 0.2% of the total examined individuals, respectively, in Bashagard district. Regarding to Ghale-Ganj district, about 0.9% of the individuals had IgG -specific antibody to P. vivax at the first and second surveys, but at the third survey 0.45% of the participants had positive antibody to P. vivax parasite. IgG -specific antibody to P. falciparum was detected in 0.2% of the participants at the first and follow-up surveys. The overall regional differences were not statistically significant (P > 0.05). CONCLUSION: Taken together, the lack of asymptomatic carrier with the evidence of extremely low sero-positive to both P. vivax and P. falciparum among examined individuals supported the limited recent transmission in the studied areas and, therefore, these parts of Iran have potential to eliminate the disease in the next few years. However, continued follow up and action are still needed in both studied areas and also in their neighbouring province, Sistan and Baluchistan, which has the highest reported cases of malaria in Iran and also, has the largest border line with Afghanistan and Pakistan, with no elimination activities. This data will provide useful information for managing elimination activities in Iran.

Project description:Background: Antimalarial antibody measurements are useful because they reflect historical and recent exposure to malaria. As such, they may provide additional information to assess ongoing transmission in low endemic or pre-elimination settings where cases are rare. In addition, the absence of antibody responses in certain individuals can indicate the cessation of transmission. Commercial malaria enzyme-linked immunosorbent assays (ELISA) detect antimalarial antibodies and are commonly used to screen blood donations for possible malaria infection. However, there is no standardized test to detect antimalarial antibodies for epidemiological use. Here we compared five commercially available ELISA kits (Trinity Biotech, newbio, DiaPro, Cellabs, and NovaTec) in search of a standardized tool for supporting claims of absence of malaria transmission. For comparison, a research-based (RB) ELISA protocol was performed alongside the commercial kits. Results: The commercial kits were first compared using serum samples from known malaria-unexposed individuals (n = 223) and Toxoplasma-infected individuals (n = 191) to assess specificity and cross-reactivity against non-malaria infections. In addition, 134 samples from ≥10-year-olds collected in a hyperendemic region in the Gambia in the early 1990s were used to assess sensitivity. Three out of five kits showed high sensitivity (90-92%), high specificity (98-99%), low cross-reactivity (0-3%) and were considered user-friendly (Trinity Biotech, newbio and NovaTec). Two of these kits (Trinity Biotech and NovaTec) were taken forward for epidemiological evaluation and results were compared to those using the RB-ELISA. Samples from two pre-elimination settings (Praia, Cape Verde; n = 1,396, and Bataan, the Philippines; n = 1,824) were tested. Serological results from both the Trinity Biotech kit and the RB-ELISA concurred with recent passively detected case counts in both settings. Results from the Trinity Biotech kit reflected a significant decrease in the number of reported cases in Bataan in the 1990s better than the RB-ELISA. Results from the NovaTec kit did not reflect transmission patterns in either setting. Conclusion: The Trinity Biotech commercial ELISA kit was considered reliable for epidemiological use and accurately described transmission patterns in two (previously) malaria endemic settings. The use of this simple and standardized serological tool may aid national control and elimination programs by confirming that regions are free from malaria.

Project description:BackgroundReactive case detection could be a powerful tool in malaria elimination, as it selectively targets transmission pockets. However, field operations have yet to demonstrate under which conditions, if any, reactive case detection is best poised to push a region to elimination. This study uses mathematical modelling to assess how baseline transmission intensity and local interconnectedness affect the impact of reactive activities in the context of other possible intervention packages.MethodsCommunities in Southern Province, Zambia, where elimination operations are currently underway, were used as representatives of three archetypes of malaria transmission: low-transmission, high household density; high-transmission, low household density; and high-transmission, high household density. Transmission at the spatially-connected household level was simulated with a dynamical model of malaria transmission, and local variation in vectorial capacity and intervention coverage were parameterized according to data collected from the area. Various potential intervention packages were imposed on each of the archetypical settings and the resulting likelihoods of elimination by the end of 2020 were compared.ResultsSimulations predict that success of elimination campaigns in both low- and high-transmission areas is strongly dependent on stemming the flow of imported infections, underscoring the need for regional-scale strategies capable of reducing transmission concurrently across many connected areas. In historically low-transmission areas, treatment of clinical malaria should form the cornerstone of elimination operations, as most malaria infections in these areas are symptomatic and onward transmission would be mitigated through health system strengthening; reactive case detection has minimal impact in these settings. In historically high-transmission areas, vector control and case management are crucial for limiting outbreak size, and the asymptomatic reservoir must be addressed through reactive case detection or mass drug campaigns.ConclusionsReactive case detection is recommended only for settings where transmission has recently been reduced rather than all low-transmission settings. This is demonstrated in a modelling framework with strong out-of-sample accuracy across a range of transmission settings while including methodologies for understanding the most resource-effective allocations of health workers. This approach generalizes to providing a platform for planning rational scale-up of health systems based on locally-optimized impact according to simplified stratification.

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Inference of malaria reproduction numbers in three elimination settings by combining temporal data and distance metrics.

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