Dataset Information

Modelling the spatial risk of malaria through probability distribution of Anopheles maculipennis s.l. and imported cases.

ABSTRACT: Malaria remains one of the most important infectious diseases globally due to its high incidence and mortality rates. The influx of infected cases from endemic to non-endemic malaria regions like Europe has resulted in a public health concern over sporadic local outbreaks. This is facilitated by the continued presence of competent Anopheles vectors in non-endemic countries.We modelled the potential distribution of the main malaria vector across Spain using the ensemble of eight modelling techniques based on environmental parameters and the Anopheles maculipennis s.l. presence/absence data collected from 2000 to 2020. We then combined this map with the number of imported malaria cases in each municipality to detect the geographic hot spots with a higher risk of local malaria transmission.The malaria vector occurred preferentially in irrigated lands characterized by warm climate conditions and moderate annual precipitation. Some areas surrounding irrigated lands in northern Spain (e.g. Zaragoza, Logroño), mainland areas (e.g. Madrid, Toledo) and in the South (e.g. Huelva), presented a significant likelihood of A. maculipennis s.l. occurrence, with a large overlap with the presence of imported cases of malaria.While the risk of malaria re-emergence in Spain is low, it is not evenly distributed throughout the country. The four recorded local cases of mosquito-borne transmission occurred in areas with a high overlap of imported cases and mosquito presence. Integrating mosquito distribution with human incidence cases provides an effective tool for the quantification of large-scale geographic variation in transmission risk and pinpointing priority areas for targeted surveillance and prevention.

SUBMITTER: Taheri S

PROVIDER: S-EPMC11073426 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Modelling the spatial risk of malaria through probability distribution of <i>Anopheles maculipennis</i> s.l. and imported cases.

Taheri Shirin S González Mikel Alexander MA Ruiz-López María José MJ Magallanes Sergio S Delacour-Estrella Sarah S Lucientes Javier J Bueno-Marí Rubén R Martínez-de la Puente Josué J Bravo-Barriga Daniel D Frontera Eva E Polina Alejandro A Martinez-Barciela Yasmina Y Pereira José Manuel JM Garrido Josefina J Aranda Carles C Marzal Alfonso A Ruiz-Arrondo Ignacio I Oteo José Antonio JA Ferraguti Martina M Gutíerrez-López Rafael R Estrada Rosa R Miranda Miguel Ángel MÁ Barceló Carlos C Morchón Rodrigo R Montalvo Tomas T Gangoso Laura L Goiri Fátima F García-Pérez Ana L AL Ruiz Santiago S Fernandez-Martinez Beatriz B Gómez-Barroso Diana D Figuerola Jordi J

Emerging microbes & infections 20240502 1

Malaria remains one of the most important infectious diseases globally due to its high incidence and mortality rates. The influx of infected cases from endemic to non-endemic malaria regions like Europe has resulted in a public health concern over sporadic local outbreaks. This is facilitated by the continued presence of competent <i>Anopheles</i> vectors in non-endemic countries.We modelled the potential distribution of the main malaria vector across Spain using the ensemble of eight modelling ...[more]

PMID: 38618930

Similar Datasets

Project description:BackgroundAggravation of insecticide resistance in malaria vectors is threatening the efforts to control malaria by reducing the efficacy of insecticide-based interventions hence needs to be closely monitored. This study investigated the intensity of insecticide resistance of two major malaria vectors An. funestus sensu stricto (s.s.) and An. gambiae sensu lato (s.l.) collected in southern Ghana and assessed the bio-efficacy of several long-lasting insecticidal nets (LLINs) against these mosquito populations.MethodsThe insecticide susceptibility profiles of Anopheles funestus s.s. and Anopheles gambiae s.l. populations from Obuasi region (Atatam), southern Ghana were characterized and the bio-efficacy of some LLINs was assessed to determine the impact of insecticide resistance on the effectiveness of these tools. Furthermore, molecular markers associated with insecticide resistance in both species were characterized in the F0 and F1 populations using PCR and qPCR methods.ResultsAnopheles funestus s.s. was the predominant species and was resistant to pyrethroids, organochlorine and carbamate insecticides, but fully susceptible to organophosphates. An. gambiae s.l. was resistant to all four insecticide classes. High intensity of resistance to 5 × and 10 × the discriminating concentration (DC) of pyrethroids was observed in both species inducing a considerable loss of efficacy of long-lasting insecticidal nets (LLINs). Temporal expression analysis revealed a massive 12-fold increase in expression of the CYP6P4a cytochrome P450 gene in An. funestus s.s., initially from a fold change of 41 (2014) to 500 (2021). For both species, the expression of candidate genes did not vary according to discriminating doses. An. gambiae s.l. exhibited high frequencies of target-site resistance including Vgsc-1014F (90%) and Ace-1 (50%) while these mutations were absent in An. funestus s.s.ConclusionsThe multiple and high intensity of resistance observed in both malaria vectors highlights the need to implement resistance management strategies and the introduction of new insecticide chemistries.

Project description:BackgroundA key issue in achieving and sustaining malaria elimination is the need to prevent local transmission arising from imported cases of malaria. The likelihood of this occurring depends on a range of local factors, and these can be used to allocate resources to contain transmission. Therefore, a risk assessment and management strategy is required to identify risk indexes for malaria transmission when imported cases occur. These risks also need to be quantified and combined to give a weighted risk index score. This can then be used to allocate the resources to each administrative region to prevent transmission according to the degree of risk.MethodsA list of potential risk indexes were generated from a literature review, expert consultation and panel discussion. These were initially classified into 4 first-level indexes including infection source, transmitting conditions, population vulnerability and control capacity. Each of these was then expanded into more detailed second-level indexes. The Delphi method was then used to obtain expert opinion to review and revise these risk indexes over two consecutive rounds to quantify agreement among experts as to their level of importance. Risk indexes were included in the final Transmission Risk Framework if they achieved a weighted importance score ≥ 4. The Analytic Hierarchy Process was then used to calculate the weight allocated to each of the final risk indexes. This was then used to create an assessment framework that can be used to evaluate local transmission risk in different areas.ResultsTwo rounds of Delphi consultation were conducted. Twenty-three experts were used at each round with 100% recovery rate of participant questionnaires. The coordination coefficients (W) for the two rounds of Delphi consultation were 0.341 and 0.423, respectively (P < 0.05). Three first-level indexes and 13 second-level indexes were identified. The Analytic Hierarchy Process was performed to calculate the weight of the indexes. For the first-level indexes, infection source, transmitting conditions, and control capacity, the index weight was 0.5396, 0.2970 and 0.1634 respectively. For the three top second-level indexes, number of imported malaria cases, Anopheles species, and awareness of timely medical visit of patient, the index weight was 0.3382, 0.2475, and 0.1509 respectively.ConclusionsAn indexed system of transmission risk assessment for imported malaria was established using the Delphi method and the Analytic Hierarchy Process. This was assessed to be an objective and practical tool for assessing transmission risk from imported cases of malaria into China.

Dataset Information

Modelling the spatial risk of malaria through probability distribution of Anopheles maculipennis s.l. and imported cases.

Publications

Modelling the spatial risk of malaria through probability distribution of <i>Anopheles maculipennis</i> s.l. and imported cases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets