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- Farooq, Zia, et al.
(författare)
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Artificial intelligence to predict West Nile virus outbreaks with eco-climatic drivers
- 2022
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Ingår i: The Lancet Regional Health - Europe. - : Elsevier BV. - 2666-7762. ; 17
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Tidskriftsartikel (refereegranskat)abstract
- 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).
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| 3. |
- Farooq, Zia, et al.
(författare)
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European projections of West Nile virus transmission under climate change scenarios
- 2023
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Ingår i: One Health. - : Elsevier. - 2352-7714. ; 16
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
- Liu, Ying, et al.
(författare)
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Reviewing estimates of the basic reproduction number for dengue, Zika and chikungunya across global climate zones
- 2020
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Ingår i: Environmental Research. - : Elsevier. - 0013-9351 .- 1096-0953. ; 182
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Globally, dengue, Zika virus, and chikungunya are important viral mosquito-borne diseases that infect millions of people annually. Their geographic range includes not only tropical areas but also sub-tropical and temperate zones such as Japan and Italy. The relative severity of these arboviral disease outbreaks can vary depending on the setting. In this study we explore variation in the epidemiologic potential of outbreaks amongst these climatic zones and arboviruses in order to elucidate potential reasons behind such differences.METHODOLOGY: We reviewed the peer-reviewed literature (PubMed) to obtain basic reproduction number (R0) estimates for dengue, Zika virus, and chikungunya from tropical, sub-tropical and temperate regions. We also computed R0 estimates for temperate and sub-tropical climate zones, based on the outbreak curves in the initial outbreak phase. Lastly we compared these estimates across climate zones, defined by latitude.RESULTS: Of 2115 studies, we reviewed the full text of 128 studies and included 65 studies in our analysis. Our results suggest that the R0 of an arboviral outbreak depends on climate zone, with lower R0 estimates, on average, in temperate zones (R0 = 2.03) compared to tropical (R0 = 3.44) and sub-tropical zones (R0 = 10.29). The variation in R0 was considerable, ranging from 0.16 to 65. The largest R0 was for dengue (65) and was estimated by the Ross-Macdonald model in the tropical zone, whereas the smallest R0 (0.16) was for Zika virus and was estimated statistically from an outbreak curve in the sub-tropical zone.CONCLUSIONS: The results indicate climate zone to be an important determinant of the basic reproduction number, R0, for dengue, Zika virus, and chikungunya. The role of other factors as determinants of R0, such as methods, environmental and social conditions, and disease control, should be further investigated. The results suggest that R0 may increase in temperate regions in response to global warming, and highlight the increasing need for strengthening preparedness and control activities.
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| 7. |
- Rocklöv, Joacim, Professor, 1979-, et al.
(författare)
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Decision-support tools to build climate resilience against emerging infectious diseases in Europe and beyond
- 2023
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Ingår i: The Lancet Regional Health. - : Elsevier. - 2666-7762. ; 32
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Forskningsöversikt (refereegranskat)abstract
- 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.
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| 8. |
- Rocklöv, Joacim, Professor, 1979-, et al.
(författare)
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Using Big Data to Monitor the Introduction and Spread of Chikungunya, Europe, 2017
- 2019
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Ingår i: Emerging Infectious Diseases. - : Centers for Disease Control and Prevention (CDC). - 1080-6040 .- 1080-6059. ; 25:6, s. 1041-1049
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Tidskriftsartikel (refereegranskat)abstract
- With regard to fully harvesting the potential of big data, public health lags behind other fields. To determine this potential, we applied big data (air passenger volume from international areas with active chikungunya transmission, Twitter data, and vectorial capacity estimates of Aedes albopictus mosquitoes) to the 2017 chikungunya outbreaks in Europe to assess the risks for virus transmission, virus importation, and short-range dispersion from the outbreak foci. We found that indicators based on voluminous and velocious data can help identify virus dispersion from outbreak foci and that vector abundance and vectorial capacity estimates can provide information on local climate suitability for mosquitoborne outbreaks. In contrast, more established indicators based on Wikipedia and Google Trends search strings were less timely. We found that a combination of novel and disparate datasets can be used in real time to prevent and control emerging and reemerging infectious diseases.
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