Background Future distribution of dengue risk is usually predicted based on predicted climate changes using general circulation models(GCMs).However,it is difficult to validate the GCM results and assess the uncertain...Background Future distribution of dengue risk is usually predicted based on predicted climate changes using general circulation models(GCMs).However,it is difficult to validate the GCM results and assess the uncertainty of the predictions.The observed changes in climate may be very different from the GCM results.We aim to utilize trends in observed climate dynamics to predict future risks of Aedes albopictus in China.Methods We collected Ae.albopictus surveillance data and observed climate records from 80 meteorological stations from 1970 to 2021.We analyzed the trends in climate change in China and made predictions on future climate for the years 2050 and 2080 based on trend analyses.We analyzed the relationship between climatic variables and the prevalence of Ae.albopictus in different months/seasons.We built a classification tree model(based on the average of 999 runs of classification and regression tree analyses)to predict the monthly/seasonal Ae.albopictus distribution based on the average climate from 1970 to 2000 and assessed the contributions of different climatic variables to the Ae.albopictus distribution.Using these models,we projected the future distributions of Ae.albopictus for 2050 and 2080.Results The study included Ae.albopictus surveillance from 259 sites in China found that winter to early spring(November–February)temperatures were strongly correlated with Ae.albopictus prevalence(prediction accuracy ranges 93.0–98.8%)—the higher the temperature the higher the prevalence,while precipitation in summer(June–September)was important predictor for Ae.albopictus prevalence.The machine learning tree models predicted the current prevalence of Ae.albopictus with high levels of agreement(accuracy>90%and Kappa agreement>80%for all 12 months).Overall,winter temperature contributed the most to Ae.albopictus distribution,followed by summer precipitation.An increase in temperature was observed from 1970 to 2021 in most places in China,and annual change rates varied substantially from-0.22℃/year to 0.58℃/year among sites,with the largest increase in temperature occurring from February to April(an annual increase of 1.4–4.7℃ in monthly mean,0.6–4.0℃ in monthly minimum,and 1.3–4.3℃ in monthly maximum temperature)and the smallest in November and December.Temperature increases were lower in the tropics/subtropics(1.5–2.3℃ from February–April)compared to the high-latitude areas(2.6–4.6℃ from February–April).The projected temperatures in 2050 and 2080 by this study were approximately 1–1.5℃ higher than those projected by GCMs.The estimated current Ae.albopictus risk distribution had a northern boundary of north-central China and the southern edge of northeastern China,with a risk period of June–September.The projected future Ae.albopictus risks in 2050 and 2080 cover nearly all of China,with an expanded risk period of April–October.The current at-risk population was estimated to be 960 million and the future at-risk population was projected to be 1.2 billion.Conclusions The magnitude of climate change in China is likely to surpass GCM predictions.Future dengue risks will expand to cover nearly all of China if current climate trends continue.展开更多
Background:Aedes albopictus is a highly invasive mosquito species and a major vector of numerous viral pathogens.Many recent dengue fever outbreaks in China have been caused solely by the vector.Mapping of the potenti...Background:Aedes albopictus is a highly invasive mosquito species and a major vector of numerous viral pathogens.Many recent dengue fever outbreaks in China have been caused solely by the vector.Mapping of the potential distribution ranges of Ae.albopictus is crucial for epidemic preparedness and the monitoring of vector populations for disease control.Climate is a key factor influencing the distribution of the species.Despite field studies indicating seasonal population variations,very little modeling work has been done to analyze how environmental conditions influence the seasonality of Ae.albopictus.The aim of the present study was to develop a model based on available observations,climatic and environmental data,and machine learning methods for the prediction of the potential seasonal ranges of Ae.albopictus in China.Methods:We collected comprehensive up-to-date surveillance data in China,particularly records from the northern distribution margin of Ae.albopictus.All records were assigned long-term(1970–2000)climatic data averages based on the WorldClim 2.0 data set.Machine learning regression tree models were developed using a 10-fold crossvalidation method to predict the potential seasonal(or monthly)distribution ranges of Ae.albopictus in China at high resolution based on environmental conditions.The models were assessed based on sensitivity,specificity,and accuracy,using area under curve(AUC).WorldClim 2.0 and climatic and environmental data were used to produce environmental conduciveness(probability)prediction surfaces.Predicted probabilities were generated based on the averages of the 10 models.Results:During 1998–2017,Ae.albopictus was observed at 200 out of the 242 localities surveyed.In addition,at least 15 new Ae.albopictus occurrence sites lay outside the potential ranges that have been predicted using models previously.The average accuracy was 98.4%(97.1–99.5%),and the average AUC was 99.1%(95.6–99.9%).The predicted Ae.albopictus distribution in winter(December–February)was limited to a small subtropical-tropical area of China,and Ae.albopictus was predicted to occur in northern China only during the short summer season(usually June–September).The predicted distribution areas in summer could reach northeastern China bordering Russia and the eastern part of the Qinghai-Tibet Plateau in southwestern China.Ae.albopictus could remain active in expansive areas from central to southern China in October and November.Conclusions:Climate and environmental conditions are key factors influencing the seasonal distribution of Ae.albopictus in China.The areas predicted to potentially host Ae.albopictus seasonally in the present study could reach northeastern China and the eastern slope of the Qinghai-Tibet Plateau.Our results present new evidence and suggest the expansion of systematic vector population monitoring activities and regular re-assessment of epidemic risk potential.展开更多
The issues of pyrethroid resistance and outdoor malaria parasite transmission have prompted the WHO to call for the development and adoption of viable alternative vector control methods.Larval source management is one...The issues of pyrethroid resistance and outdoor malaria parasite transmission have prompted the WHO to call for the development and adoption of viable alternative vector control methods.Larval source management is one of the core malaria vector interventions recommended by the Ministry of Health in many African countries,but it is rarely implemented due to concerns on its cost-effectiveness.New long-lasting microbial larvicide can be a promising cost-effective supplement to current vector control and elimination methods because microbial larvicide uses killing mechanisms different from pyrethroids and other chemical insecticides.It has been shown to be effective in reducing the overall vector abundance and thus both indoor and outdoor transmission.In our opinion,the long-lasting formulation can potentially reduce the cost of larvicide field application,and should be evaluated for its cost-effectiveness,resistance development,and impact on non-target organisms when integrating with other malaria vector control measures.In this opinion,we highlight that long-lasting microbial larvicide can be a potential cost-effective product that complements current front-line long-lasting insecticidal nets(LLINs)and indoor residual spraying(IRS)programs for malaria control and elimination.Microbial larviciding targets immature mosquitoes,reduces both indoor and outdoor transmission and is not affected by vector resistance to synthetic insecticides.This control method is a shift from the conventional LLINs and IRS programs that mainly target indoor-biting and resting adult mosquitoes.展开更多
文摘Background Future distribution of dengue risk is usually predicted based on predicted climate changes using general circulation models(GCMs).However,it is difficult to validate the GCM results and assess the uncertainty of the predictions.The observed changes in climate may be very different from the GCM results.We aim to utilize trends in observed climate dynamics to predict future risks of Aedes albopictus in China.Methods We collected Ae.albopictus surveillance data and observed climate records from 80 meteorological stations from 1970 to 2021.We analyzed the trends in climate change in China and made predictions on future climate for the years 2050 and 2080 based on trend analyses.We analyzed the relationship between climatic variables and the prevalence of Ae.albopictus in different months/seasons.We built a classification tree model(based on the average of 999 runs of classification and regression tree analyses)to predict the monthly/seasonal Ae.albopictus distribution based on the average climate from 1970 to 2000 and assessed the contributions of different climatic variables to the Ae.albopictus distribution.Using these models,we projected the future distributions of Ae.albopictus for 2050 and 2080.Results The study included Ae.albopictus surveillance from 259 sites in China found that winter to early spring(November–February)temperatures were strongly correlated with Ae.albopictus prevalence(prediction accuracy ranges 93.0–98.8%)—the higher the temperature the higher the prevalence,while precipitation in summer(June–September)was important predictor for Ae.albopictus prevalence.The machine learning tree models predicted the current prevalence of Ae.albopictus with high levels of agreement(accuracy>90%and Kappa agreement>80%for all 12 months).Overall,winter temperature contributed the most to Ae.albopictus distribution,followed by summer precipitation.An increase in temperature was observed from 1970 to 2021 in most places in China,and annual change rates varied substantially from-0.22℃/year to 0.58℃/year among sites,with the largest increase in temperature occurring from February to April(an annual increase of 1.4–4.7℃ in monthly mean,0.6–4.0℃ in monthly minimum,and 1.3–4.3℃ in monthly maximum temperature)and the smallest in November and December.Temperature increases were lower in the tropics/subtropics(1.5–2.3℃ from February–April)compared to the high-latitude areas(2.6–4.6℃ from February–April).The projected temperatures in 2050 and 2080 by this study were approximately 1–1.5℃ higher than those projected by GCMs.The estimated current Ae.albopictus risk distribution had a northern boundary of north-central China and the southern edge of northeastern China,with a risk period of June–September.The projected future Ae.albopictus risks in 2050 and 2080 cover nearly all of China,with an expanded risk period of April–October.The current at-risk population was estimated to be 960 million and the future at-risk population was projected to be 1.2 billion.Conclusions The magnitude of climate change in China is likely to surpass GCM predictions.Future dengue risks will expand to cover nearly all of China if current climate trends continue.
基金This study was supported by grants from the National Natural Science Foundation of China(No:31630011)Science and Technology Plan Project of Guangzhou city(No.201804020084)+1 种基金Natural Science Foundation of Guangdong province(No.2017A030313625)The funders had no role in study design,data collection and analysis,decision to publish,or preparation of the manuscript.
文摘Background:Aedes albopictus is a highly invasive mosquito species and a major vector of numerous viral pathogens.Many recent dengue fever outbreaks in China have been caused solely by the vector.Mapping of the potential distribution ranges of Ae.albopictus is crucial for epidemic preparedness and the monitoring of vector populations for disease control.Climate is a key factor influencing the distribution of the species.Despite field studies indicating seasonal population variations,very little modeling work has been done to analyze how environmental conditions influence the seasonality of Ae.albopictus.The aim of the present study was to develop a model based on available observations,climatic and environmental data,and machine learning methods for the prediction of the potential seasonal ranges of Ae.albopictus in China.Methods:We collected comprehensive up-to-date surveillance data in China,particularly records from the northern distribution margin of Ae.albopictus.All records were assigned long-term(1970–2000)climatic data averages based on the WorldClim 2.0 data set.Machine learning regression tree models were developed using a 10-fold crossvalidation method to predict the potential seasonal(or monthly)distribution ranges of Ae.albopictus in China at high resolution based on environmental conditions.The models were assessed based on sensitivity,specificity,and accuracy,using area under curve(AUC).WorldClim 2.0 and climatic and environmental data were used to produce environmental conduciveness(probability)prediction surfaces.Predicted probabilities were generated based on the averages of the 10 models.Results:During 1998–2017,Ae.albopictus was observed at 200 out of the 242 localities surveyed.In addition,at least 15 new Ae.albopictus occurrence sites lay outside the potential ranges that have been predicted using models previously.The average accuracy was 98.4%(97.1–99.5%),and the average AUC was 99.1%(95.6–99.9%).The predicted Ae.albopictus distribution in winter(December–February)was limited to a small subtropical-tropical area of China,and Ae.albopictus was predicted to occur in northern China only during the short summer season(usually June–September).The predicted distribution areas in summer could reach northeastern China bordering Russia and the eastern part of the Qinghai-Tibet Plateau in southwestern China.Ae.albopictus could remain active in expansive areas from central to southern China in October and November.Conclusions:Climate and environmental conditions are key factors influencing the seasonal distribution of Ae.albopictus in China.The areas predicted to potentially host Ae.albopictus seasonally in the present study could reach northeastern China and the eastern slope of the Qinghai-Tibet Plateau.Our results present new evidence and suggest the expansion of systematic vector population monitoring activities and regular re-assessment of epidemic risk potential.
基金This work was supported by the National Institutes of Health(R01 A1050243,U19 AI129326 and D43 TW001505).
文摘The issues of pyrethroid resistance and outdoor malaria parasite transmission have prompted the WHO to call for the development and adoption of viable alternative vector control methods.Larval source management is one of the core malaria vector interventions recommended by the Ministry of Health in many African countries,but it is rarely implemented due to concerns on its cost-effectiveness.New long-lasting microbial larvicide can be a promising cost-effective supplement to current vector control and elimination methods because microbial larvicide uses killing mechanisms different from pyrethroids and other chemical insecticides.It has been shown to be effective in reducing the overall vector abundance and thus both indoor and outdoor transmission.In our opinion,the long-lasting formulation can potentially reduce the cost of larvicide field application,and should be evaluated for its cost-effectiveness,resistance development,and impact on non-target organisms when integrating with other malaria vector control measures.In this opinion,we highlight that long-lasting microbial larvicide can be a potential cost-effective product that complements current front-line long-lasting insecticidal nets(LLINs)and indoor residual spraying(IRS)programs for malaria control and elimination.Microbial larviciding targets immature mosquitoes,reduces both indoor and outdoor transmission and is not affected by vector resistance to synthetic insecticides.This control method is a shift from the conventional LLINs and IRS programs that mainly target indoor-biting and resting adult mosquitoes.
