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Rocklöv, Joacim, ProfessorORCID iD iconorcid.org/0000-0003-4030-0449
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Publications (10 of 196) Show all publications
Tozan, Y., Sewe, M. O., Kim, S. & Rocklöv, J. (2023). A methodological framework for economic evaluation of operational response to vector-borne diseases based on early warning systems. American Journal of Tropical Medicine and Hygiene, 108(3), 627-633
Open this publication in new window or tab >>A methodological framework for economic evaluation of operational response to vector-borne diseases based on early warning systems
2023 (English)In: American Journal of Tropical Medicine and Hygiene, ISSN 0002-9637, E-ISSN 1476-1645, Vol. 108, no 3, p. 627-633Article in journal (Refereed) Published
Abstract [en]

Despite significant advances in improving the predictive models for vector-borne diseases, only a few countries have integrated an early warning system (EWS) with predictive and response capabilities into their disease surveillance systems. The limited understanding of forecast performance and uncertainties by decision-makers is one of the primary factors that precludes its operationalization in preparedness and response planning. Further, predictive models exhibit a decrease in forecast skill with longer lead times, a trade-off between forecast accuracy and timeliness and effectiveness of action. This study presents a methodological framework to evaluate the economic value of EWS-triggered responses from the health system perspective. Assuming an operational EWS in place, the framework makes explicit the trade-offs between forecast accuracy, timeliness of action, effectiveness of response, and costs, and uses the net benefit analysis, which measures the benefits of taking action minus the associated costs. Uncertainty in disease forecasts and other parameters is accounted for through probabilistic sensitivity analysis. The output is the probability distribution of the net benefit estimates at given forecast lead times. A non-negative net benefit and the probability of yielding such are considered a general signal that the EWS-triggered response at a given lead time is economically viable. In summary, the proposed framework translates uncertainties associated with disease forecasts and other parameters into decision uncertainty by quantifying the economic risk associated with operational response to vector-borne disease events of potential importance predicted by an EWS. The goal is to facilitate a more informed and transparent public health decision-making under uncertainty.

Place, publisher, year, edition, pages
American Society of Tropical Medicine and Hygiene, 2023
Keywords
Cost-Benefit Analysis, Humans, Probability, Uncertainty
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:umu:diva-205642 (URN)10.4269/ajtmh.22-0471 (DOI)000976649800031 ()36646075 (PubMedID)2-s2.0-85149173803 (Scopus ID)
Available from: 2023-03-14 Created: 2023-03-14 Last updated: 2023-09-05Bibliographically approved
Armando, C. J., Rocklöv, J., Sidat, M., Tozan, Y., Mavume, A. F., Bunker, A. & Sewe, M. O. (2023). Climate variability, socio-economic conditions and vulnerability to malaria infections in Mozambique 2016–2018: a spatial temporal analysis. Frontiers In Public Health, 11, Article ID 1162535.
Open this publication in new window or tab >>Climate variability, socio-economic conditions and vulnerability to malaria infections in Mozambique 2016–2018: a spatial temporal analysis
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2023 (English)In: Frontiers In Public Health, ISSN 2296-2565, Vol. 11, article id 1162535Article in journal (Refereed) Published
Abstract [en]

Background: Temperature, precipitation, relative humidity (RH), and Normalized Different Vegetation Index (NDVI), influence malaria transmission dynamics. However, an understanding of interactions between socioeconomic indicators, environmental factors and malaria incidence can help design interventions to alleviate the high burden of malaria infections on vulnerable populations. Our study thus aimed to investigate the socioeconomic and climatological factors influencing spatial and temporal variability of malaria infections in Mozambique.

Methods: We used monthly malaria cases from 2016 to 2018 at the district level. We developed an hierarchical spatial–temporal model in a Bayesian framework. Monthly malaria cases were assumed to follow a negative binomial distribution. We used integrated nested Laplace approximation (INLA) in R for Bayesian inference and distributed lag nonlinear modeling (DLNM) framework to explore exposure-response relationships between climate variables and risk of malaria infection in Mozambique, while adjusting for socioeconomic factors.

Results: A total of 19,948,295 malaria cases were reported between 2016 and 2018 in Mozambique. Malaria risk increased with higher monthly mean temperatures between 20 and 29°C, at mean temperature of 25°C, the risk of malaria was 3.45 times higher (RR 3.45 [95%CI: 2.37–5.03]). Malaria risk was greatest for NDVI above 0.22. The risk of malaria was 1.34 times higher (1.34 [1.01–1.79]) at monthly RH of 55%. Malaria risk reduced by 26.1%, for total monthly precipitation of 480 mm (0.739 [95%CI: 0.61–0.90]) at lag 2 months, while for lower total monthly precipitation of 10 mm, the risk of malaria was 1.87 times higher (1.87 [1.30–2.69]). After adjusting for climate variables, having lower level of education significantly increased malaria risk (1.034 [1.014–1.054]) and having electricity (0.979 [0.967–0.992]) and sharing toilet facilities (0.957 [0.924–0.991]) significantly reduced malaria risk.

Conclusion: Our current study identified lag patterns and association between climate variables and malaria incidence in Mozambique. Extremes in climate variables were associated with an increased risk of malaria transmission, peaks in transmission were varied. Our findings provide insights for designing early warning, prevention, and control strategies to minimize seasonal malaria surges and associated infections in Mozambique a region where Malaria causes substantial burden from illness and deaths.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2023
Keywords
Bayesian, climate variability, DHS, DLNM, INLA, malaria vulnerability, Mozambique, spatio-temporal
National Category
Public Health, Global Health, Social Medicine and Epidemiology Occupational Health and Environmental Health
Research subject
climate change; Infectious Diseases
Identifiers
urn:nbn:se:umu:diva-211167 (URN)10.3389/fpubh.2023.1162535 (DOI)001005894100001 ()37325319 (PubMedID)2-s2.0-85162000346 (Scopus ID)
Funder
Sida - Swedish International Development Cooperation Agency
Available from: 2023-07-04 Created: 2023-07-04 Last updated: 2023-07-04Bibliographically approved
Rocklöv, J., Semenza, J. C., Dasgupta, S., Robinson, E. J. .., Abd El Wahed, A., Alcayna, T., . . . Lowe, R. (2023). Decision-support tools to build climate resilience against emerging infectious diseases in Europe and beyond. The Lancet Regional Health: Europe, 32, Article ID 100701.
Open this publication in new window or tab >>Decision-support tools to build climate resilience against emerging infectious diseases in Europe and beyond
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2023 (English)In: The Lancet Regional Health: Europe, E-ISSN 2666-7762, Vol. 32, article id 100701Article, review/survey (Refereed) Published
Abstract [en]

Climate change is one of several drivers of recurrent outbreaks and geographical range expansion of infectious diseases in Europe. We propose a framework for the co-production of policy-relevant indicators and decision-support tools that track past, present, and future climate-induced disease risks across hazard, exposure, and vulnerability domains at the animal, human, and environmental interface. This entails the co-development of early warning and response systems and tools to assess the costs and benefits of climate change adaptation and mitigation measures across sectors, to increase health system resilience at regional and local levels and reveal novel policy entry points and opportunities. Our approach involves multi-level engagement, innovative methodologies, and novel data streams. We take advantage of intelligence generated locally and empirically to quantify effects in areas experiencing rapid urban transformation and heterogeneous climate-induced disease threats. Our goal is to reduce the knowledge-to-action gap by developing an integrated One Health—Climate Risk framework.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Adaptation, Climate change, Climate policy, Co-production, Human health, Infectious disease, Mitigation, One Health, Planetary health
National Category
Public Health, Global Health, Social Medicine and Epidemiology Climate Research
Identifiers
urn:nbn:se:umu:diva-214534 (URN)10.1016/j.lanepe.2023.100701 (DOI)37583927 (PubMedID)2-s2.0-85170215685 (Scopus ID)
Funder
EU, Horizon Europe, 101057554
Note

Contributor: IDAlert Consortium.

Available from: 2023-09-21 Created: 2023-09-21 Last updated: 2023-09-21Bibliographically approved
Farooq, Z., Sjödin, H., Semenza, J. C., Tozan, Y., Sewe, M. O., Wallin, J. & Rocklöv, J. (2023). European projections of West Nile virus transmission under climate change scenarios. One Health, 16, Article ID 100509.
Open this publication in new window or tab >>European projections of West Nile virus transmission under climate change scenarios
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2023 (English)In: One Health, ISSN 2352-7714, Vol. 16, article id 100509Article in journal (Refereed) Published
Abstract [en]

West Nile virus (WNV), a mosquito-borne zoonosis, has emerged as a disease of public health concern in Europe. Recent outbreaks have been attributed to suitable climatic conditions for its vectors favoring transmission. However, to date, projections of the risk for WNV expansion under climate change scenarios is lacking. Here, we estimate the WNV-outbreaks risk for a set of climate change and socioeconomic scenarios. We delineate the potential risk-areas and estimate the growth in the population at risk (PAR). We used supervised machine learning classifier, XGBoost, to estimate the WNV-outbreak risk using an ensemble climate model and multi-scenario approach. The model was trained by collating climatic, socioeconomic, and reported WNV-infections data (2010−22) and the out-of-sample results (1950–2009, 2023–99) were validated using a novel Confidence-Based Performance Estimation (CBPE) method. Projections of area specific outbreak risk trends, and corresponding population at risk were estimated and compared across scenarios. Our results show up to 5-fold increase in West Nile virus (WNV) risk for 2040-60 in Europe, depending on geographical region and climate scenario, compared to 2000-20. The proportion of disease-reported European land areas could increase from 15% to 23-30%, putting 161 to 244 million people at risk. Across scenarios, Western Europe appears to be facing the largest increase in the outbreak risk of WNV. The increase in the risk is not linear but undergoes periods of sharp changes governed by climatic thresholds associated with ideal conditions for WNV vectors. The increased risk will require a targeted public health response to manage the expansion of WNV with climate change in Europe.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Artificial intelligence, Climate change, Climate impacts, Confidence-based performance estimation (CBPE) method, Europe, West Nile virus, WNV risk projections, XGBoost, Zoonoses
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:umu:diva-205369 (URN)10.1016/j.onehlt.2023.100509 (DOI)001004031000001 ()2-s2.0-85148667157 (Scopus ID)
Funder
Vinnova, 2020-03367Swedish Research Council Formas, 2018-01754European Commission, 101057554
Available from: 2023-03-29 Created: 2023-03-29 Last updated: 2023-09-05Bibliographically approved
Seang-Arwut, C., Hanboonsong, Y., Muenworn, V., Rocklöv, J., Haque, U., Ekalaksananan, T., . . . Overgaard, H. J. (2023). Indoor resting behavior of Aedes aegypti (Diptera: Culicidae) in northeastern Thailand. Parasites & Vectors, 16(1), Article ID 127.
Open this publication in new window or tab >>Indoor resting behavior of Aedes aegypti (Diptera: Culicidae) in northeastern Thailand
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2023 (English)In: Parasites & Vectors, E-ISSN 1756-3305, Vol. 16, no 1, article id 127Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Aedes aegypti is a vector of several arboviruses, notably dengue virus (DENV), which causes dengue fever and is often found resting indoors. Culex spp. are largely nuisance mosquitoes but can include species that are vectors of zoonotic pathogens. Vector control is currently the main method to control dengue outbreaks. Indoor residual spraying can be part of an effective vector control strategy but requires an understanding of the resting behavior. Here we focus on the indoor-resting behavior of Ae. aegypti and Culex spp. in northeastern Thailand.

METHODS: Mosquitoes were collected in 240 houses in rural and urban settings from May to August 2019 at two collection times (morning/afternoon), in four room types (bedroom, bathroom, living room and kitchen) in each house and at three wall heights (< 0.75 m, 0.75-1.5 m, > 1.5 m) using a battery-driven aspirator and sticky traps. Household characteristics were ascertained. Mosquitoes were identified as Ae. aegypti, Aedes albopictus and Culex spp. Dengue virus was detected in Ae. aegypti. Association analyses between urban/rural and within-house location (wall height, room), household variables, geckos and mosquito abundance were performed.

RESULTS: A total of 2874 mosquitoes were collected using aspirators and 1830 using sticky traps. Aedes aegypti and Culex spp. accounted for 44.78% and 53.17% of the specimens, respectively. Only 2.05% were Ae. albopictus. Aedes aegypti and Culex spp. rested most abundantly at intermediate and low heights in bedrooms or bathrooms (96.6% and 85.2% for each taxon of the total, respectively). Clothes hanging at intermediate heights were associated with higher mean numbers of Ae. aegypti in rural settings (0.81 [SEM: 0.08] vs. low: 0.61 [0.08] and high: 0.32 [0.09]). Use of larval control was associated with lower numbers of Ae. aegypti (yes: 0.61 [0.08]; no: 0.70 [0.07]). All DENV-positive Ae. aegypti (1.7%, 5 of 422) were collected in the rural areas and included specimens with single, double and even triple serotype infections.

CONCLUSIONS: Knowledge of the indoor resting behavior of adult mosquitoes and associated environmental factors can guide the choice of the most appropriate and effective vector control method. Our work suggests that vector control using targeted indoor residual spraying and/or potentially spatial repellents focusing on walls at heights lower than 1.5 m in bedrooms and bathrooms could be part of an integrated effective strategy for dengue vector control.

Place, publisher, year, edition, pages
BioMed Central (BMC), 2023
Keywords
Gecko, Height, Indoor residual spraying, Mosquito abundance, Room, Vector control
National Category
Public Health, Global Health, Social Medicine and Epidemiology Occupational Health and Environmental Health
Identifiers
urn:nbn:se:umu:diva-206944 (URN)10.1186/s13071-023-05746-9 (DOI)000971907100001 ()37060087 (PubMedID)2-s2.0-85152521296 (Scopus ID)
Funder
The Research Council of Norway, 281077
Available from: 2023-04-27 Created: 2023-04-27 Last updated: 2024-01-17Bibliographically approved
Altmejd, A., Rocklöv, J. & Wallin, J. (2023). Nowcasting COVID-19 statistics reported with delay: A case-study of Sweden and the UK. International Journal of Environmental Research and Public Health, 20(4)
Open this publication in new window or tab >>Nowcasting COVID-19 statistics reported with delay: A case-study of Sweden and the UK
2023 (English)In: International Journal of Environmental Research and Public Health, ISSN 1661-7827, E-ISSN 1660-4601, Vol. 20, no 4Article in journal (Refereed) Published
Abstract [en]

The COVID-19 pandemic has demonstrated the importance of unbiased, real-time statistics of trends in disease events in order to achieve an effective response. Because of reporting delays, real-time statistics frequently underestimate the total number of infections, hospitalizations and deaths. When studied by event date, such delays also risk creating an illusion of a downward trend. Here, we describe a statistical methodology for predicting true daily quantities and their uncertainty, estimated using historical reporting delays. The methodology takes into account the observed distribution pattern of the lag. It is derived from the "removal method"-a well-established estimation framework in the field of ecology.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
COVID-19, nowcasting, prediction
National Category
Probability Theory and Statistics Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:umu:diva-205494 (URN)10.3390/ijerph20043040 (DOI)36833733 (PubMedID)2-s2.0-85148964982 (Scopus ID)
Available from: 2023-03-14 Created: 2023-03-14 Last updated: 2023-03-17Bibliographically approved
Ramadona, A. L., Tozan, Y., Wallin, J., Lazuardi, L., Utarini, A. & Rocklöv, J. (2023). Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia: a modelling study. The Lancet Regional Health - Southeast Asia, 15, Article ID 100209.
Open this publication in new window or tab >>Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia: a modelling study
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2023 (English)In: The Lancet Regional Health - Southeast Asia, E-ISSN 2772-3682, Vol. 15, article id 100209Article in journal (Refereed) Published
Abstract [en]

Background: Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia.

Methods: We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model.

Findings: When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale.

Interpretation: The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Arbovirus, Big data, Climate services, Climate Variability, Dengue, DLNM, Early warning, Epidemic, Forecasting model, INLA, Population mobility, Rainfall, Social media, Spatiotemporal model, Temperature, Twitter, Weather
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:umu:diva-209124 (URN)10.1016/j.lansea.2023.100209 (DOI)37614350 (PubMedID)2-s2.0-85159184754 (Scopus ID)
Funder
Swedish Research Council FormasForte, Swedish Research Council for Health, Working Life and WelfareVinnova
Available from: 2023-06-07 Created: 2023-06-07 Last updated: 2023-12-05Bibliographically approved
Hollowell, T., Sewe, M. O., Rocklöv, J., Obor, D., Odhiambo, F. & Ahlm, C. (2023). Public health determinants of child malaria mortality: a surveillance study within Siaya County, Western Kenya. Malaria Journal, 22(1), Article ID 65.
Open this publication in new window or tab >>Public health determinants of child malaria mortality: a surveillance study within Siaya County, Western Kenya
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2023 (English)In: Malaria Journal, ISSN 1475-2875, E-ISSN 1475-2875, Vol. 22, no 1, article id 65Article in journal (Refereed) Published
Abstract [en]

Background: Malaria deaths among children have been declining worldwide during the last two decades. Despite preventive, epidemiologic and therapy-development work, mortality rate decline has stagnated in western Kenya resulting in persistently high child malaria morbidity and mortality. The aim of this study was to identify public health determinants influencing the high burden of malaria deaths among children in this region.

Methods: A total of 221,929 children, 111,488 females and 110,441 males, under the age of 5 years were enrolled in the Kenya Medical Research Institute/Center for Disease Control Health and Demographic Surveillance System (KEMRI/CDC HDSS) study area in Siaya County during the period 2003–2013. Cause of death was determined by use of verbal autopsy. Age-specific mortality rates were computed, and cox proportional hazard regression was used to model time to malaria death controlling for the socio-demographic factors. A variety of demographic, social and epidemiologic factors were examined.

Results: In total 8,696 (3.9%) children died during the study period. Malaria was the most prevalent cause of death and constituted 33.2% of all causes of death, followed by acute respiratory infections (26.7%) and HIV/AIDS related deaths (18.6%). There was a marked decrease in overall mortality rate from 2003 to 2013, except for a spike in the rates in 2008. The hazard of death differed between age groups with the youngest having the highest hazard of death HR 6.07 (95% CI 5.10–7.22). Overall, the risk attenuated with age and mortality risks were limited beyond 4 years of age. Longer distance to healthcare HR of 1.44 (95% CI 1.29–1.60), l ow maternal education HR 3.91 (95% CI 1.86–8.22), and low socioeconomic status HR 1.44 (95% CI 1.26–1.64) were all significantly associated with increased hazard of malaria death among children.

Conclusions: While child mortality due to malaria in the study area in Western Kenya, has been decreasing, a final step toward significant risk reduction is yet to be accomplished. This study highlights residual proximal determinants of risk which can further inform preventive actions.

Place, publisher, year, edition, pages
BioMed Central (BMC), 2023
Keywords
Child mortality, Children, Demographic surveillance, Epidemiological monitoring, Malaria, Public health
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:umu:diva-205495 (URN)10.1186/s12936-023-04502-9 (DOI)000937711600001 ()36823600 (PubMedID)2-s2.0-85148812992 (Scopus ID)
Funder
Forte, Swedish Research Council for Health, Working Life and Welfare, 206-1512
Available from: 2023-03-14 Created: 2023-03-14 Last updated: 2023-09-05Bibliographically approved
Brännström, Å., Sjödin, H. & Rocklöv, J. (2022). A Method for Estimating the Number of Infections From the Reported Number of Deaths. Frontiers In Public Health, 9, Article ID 648545.
Open this publication in new window or tab >>A Method for Estimating the Number of Infections From the Reported Number of Deaths
2022 (English)In: Frontiers In Public Health, ISSN 2296-2565, Vol. 9, article id 648545Article in journal (Refereed) Published
Abstract [en]

At the outset of an epidemic, available case data typically underestimate the total number of infections due to insufficient testing, potentially hampering public responses. Here, we present a method for statistically estimating the true number of cases with confidence intervals from the reported number of deaths and estimates of the infection fatality ratio; assuming that the time from infection to death follows a known distribution. While the method is applicable to any epidemic with a significant mortality rate, we exemplify the method by applying it to COVID-19. Our findings indicate that the number of unreported COVID-19 infections in March 2020 was likely to be at least one order of magnitude higher than the reported cases, with the degree of underestimation among the countries considered being particularly high in the United Kingdom.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2022
Keywords
COVID-19, estimating, infectives, nowcasting, surveillance
National Category
Public Health, Global Health, Social Medicine and Epidemiology Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-192376 (URN)10.3389/fpubh.2021.648545 (DOI)35111706 (PubMedID)2-s2.0-85123950757 (Scopus ID)
Available from: 2022-02-11 Created: 2022-02-11 Last updated: 2022-02-11Bibliographically approved
Farooq, Z., Rocklöv, J., Wallin, J., Abiri, N., Sewe, M. O., Sjödin, H. & Semenza, J. C. (2022). Artificial intelligence to predict West Nile virus outbreaks with eco-climatic drivers. The Lancet Regional Health: Europe, 17, Article ID 100370.
Open this publication in new window or tab >>Artificial intelligence to predict West Nile virus outbreaks with eco-climatic drivers
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2022 (English)In: The Lancet Regional Health: Europe, E-ISSN 2666-7762, Vol. 17, article id 100370Article in journal (Refereed) Published
Abstract [en]

Background: In Europe, the frequency, intensity, and geographic range of West Nile virus (WNV)-outbreaks have increased over the past decade, with a 7.2-fold increase in 2018 compared to 2017, and a markedly expanded geographic area compared to 2010. The reasons for this increase and range expansion remain largely unknown due to the complexity of the transmission pathways and underlying disease drivers. In a first, we use advanced artificial intelligence to disentangle the contribution of eco-climatic drivers to WNV-outbreaks across Europe using decade-long (2010-2019) data at high spatial resolution. Methods: We use a high-performance machine learning classifier, XGBoost (eXtreme gradient boosting) combined with state-of-the-art XAI (eXplainable artificial intelligence) methodology to describe the predictive ability and contribution of different drivers of the emergence and transmission of WNV-outbreaks in Europe, respectively. Findings: Our model, trained on 2010-2017 data achieved an AUC (area under the receiver operating characteristic curve) score of 0.97 and 0.93 when tested with 2018 and 2019 data, respectively, showing a high discriminatory power to classify a WNV-endemic area. Overall, positive summer/spring temperatures anomalies, lower water availability index (NDWI), and drier winter conditions were found to be the main determinants of WNV-outbreaks across Europe. The climate trends of the preceding year in combination with eco-climatic predictors of the first half of the year provided a robust predictive ability of the entire transmission season ahead of time. For the extraordinary 2018 outbreak year, relatively higher spring temperatures and the abundance of Culex mosquitoes were the strongest predictors, in addition to past climatic trends. Interpretation: Our AI-based framework can be deployed to trigger rapid and timely alerts for active surveillance and vector control measures in order to intercept an imminent WNV-outbreak in Europe. Funding: The work was partially funded by the Swedish Research Council FORMAS for the project ARBOPREVENT (grant agreement 2018-05973).

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Climate adaptation, Culex vectors, Early warning systems, Emerging infectious disease, Europe, forecasting, Outbreaks management, Preparedness, SHAP, West Nile virus, XGBoost
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:umu:diva-193708 (URN)10.1016/j.lanepe.2022.100370 (DOI)000796373200002 ()35373173 (PubMedID)2-s2.0-85127132481 (Scopus ID)
Funder
Swedish Research Council Formas, 2018-05973
Available from: 2022-04-25 Created: 2022-04-25 Last updated: 2023-09-05Bibliographically approved
Projects
Ansökan från David Hondula inom programmet Nordic Research Opportunity [2011-02311_VR]; Umeå UniversityPromoting local research competence, evidence and response strategies to health risks from climate change in Vietnam and Indonesia [2013-06692_VR]; Umeå UniversityBig Data supporting Public Health: Real Time Disease Forecasting and Intervention Effectiveness [2015-01540_Forte]; Umeå University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4030-0449

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