Modeling and projection of dengue fever cases in Guangzhou based on variation of weather factors

Assessing the influencing factors of dengue fever in Chinese mainland based on causal analysis

Previous studies have identified various factors affecting dengue fever, but most focus on correlations within specific regions, not establishing causality. This study uses Convergent Cross Mapping (CCM) to explore the causal relationships between nine meteorological factors and reported dengue fever cases in 14 Chinese provinces with the highest incidence. Results show that temperature and pressure have causal links with case numbers in more provinces. In Guangdong, which has the most reported cases, Partial Cross Mapping (PCM) reveals a direct causal relationship only between GDP and reported dengue fever cases, while meteorological factors influence dengue fever via their impact on mosquito populations. Principal Component Analysis (PCA) from 30 provinces further confirms the importance of temperature and pressure. Given the significant negative correlation between temperature and pressure, separate models were developed for each province using the Distributed Lag Nonlinear Model (DLNM) combined with the Generalized Additive Model (GAM), with GDP as a covariate. The results indicate that the Relative Risk (RR) increases significantly under high temperatures and low pressure within a shorter lag period. GDP significantly promotes case numbers in all provinces.

Integrated rapid risk assessment for dengue fever in settings with limited diagnostic capacity and uncertain exposure: Development of a methodological framework for Tanzania

BACKGROUND

Dengue fever is one of the world's most important re-emerging but neglected infectious diseases. We aimed to develop and evaluate an integrated risk assessment framework to enhance early detection and risk assessment of potential dengue outbreaks in settings with limited routine surveillance and diagnostic capacity.

METHODS

Our risk assessment framework utilizes the combination of various methodological components: We first focused on (I) identifying relevant clinical signals based on a case definition for suspected dengue, (II) refining the signal for potential dengue diagnosis using contextual data, and (III) determining the public health risk associated with a verified dengue signal across various hazard, exposure, and contextual indicators. We then evaluated our framework using (i) historical clinical signals with syndromic and laboratory-confirmed disease information derived from WHO's Epidemic Intelligence from Open Sources (EIOS) technology using decision tree analyses, and (ii) historical dengue outbreak data from Tanzania at the regional level from 2019 (6,795 confirmed cases) using negative binomial regression analyses adjusted for month and region. Finally, we evaluated a test signal across all steps of our integrated framework to demonstrate the implementation of our multi-method approach.

RESULTS

The result of the suspected case refinement algorithm for clinically defined syndromic cases was consistent with the laboratory-confirmed diagnosis (dengue yes or no). Regression between confirmed dengue fever cases in 2019 as the dependent variable and a site-specific public health risk score as the independent variable showed strong evidence of an increase in dengue fever cases with higher site-specific risk (rate ratio = 2.51 (95% CI = [1.76, 3.58])).

CONCLUSIONS

The framework can be used to rapidly determine the public health risk of dengue outbreaks, which is useful for planning and prioritizing interventions or for epidemic preparedness. It further allows for flexibility in its adaptation to target diseases and geographical contexts.

Health Co-Benefits of Environmental Changes in the Context of Carbon Peaking and Carbon Neutrality in China

IMPORTANCE

Climate change mitigation policies aimed at limiting greenhouse gas (GHG) emissions would bring substantial health co-benefits by directly alleviating climate change or indirectly reducing air pollution. As one of the largest developing countries and GHG emitter globally, China's carbon-peaking and carbon neutrality goals would lead to substantial co-benefits on global environment and therefore on human health. This review summarized the key findings and gaps in studies on the impact of China's carbon mitigation strategies on human health.

HIGHLIGHTS

There is a wide consensus that limiting the temperature rise well below 2 °C would markedly reduce the climate-related health impacts compared with high emission scenario, although heat-related mortalities, labor productivity reduction rates, and infectious disease morbidities would continue increasing over time as temperature rises. Further, hundreds of thousands of air pollutant-related mortalities (mainly due to PM2.5 and O3) could be avoided per year compared with the reference scenario without climate policy. Carbon reduction policies can also alleviate morbidities due to acute exposure to PM2.5. Further research with respect to morbidities attributed to nonoptimal temperature and air pollution, and health impacts attributed to precipitation and extreme weather events under current carbon policy in China or its equivalent in other developing countries is needed to improve our understanding of the disease burden in the coming decades.

CONCLUSIONS

This review provides up-to-date evidence of potential health co-benefits under Chinese carbon policies and highlights the importance of considering these co-benefits into future climate policy development in both China and other nations endeavoring carbon reductions.

Spatiotemporal analysis of imported and local dengue virus and cases in a metropolis in Southwestern China, 2013-2022

Dengue fever is a viral illness, mainly transmitted by Aedes aegypti and Aedes albopictus. With climate change and urbanisation, more urbanised areas are becoming suitable for the survival and reproduction of dengue vector, consequently are becoming suitable for dengue transmission in China. Chongqing, a metropolis in southwestern China, has recently been hit by imported and local dengue fever, experiencing its first local outbreak in 2019. However, the genetic evolution dynamics of dengue viruses and the spatiotemporal patterns of imported and local dengue cases have not yet been elucidated. Hence, this study implemented phylogenetic analyses using genomic data of dengue viruses in 2019 and 2023 and a spatiotemporal analysis of dengue cases collected from 2013 to 2022. We sequenced a total of 15 nucleotide sequences of E genes. The dengue viruses formed separate clusters and were genetically related to those from Guangdong Province, China, and countries in Southeast Asia, including Laos, Thailand, Myanmar and Cambodia. Chongqing experienced a dengue outbreak in 2019 when 168 imported and 1,243 local cases were reported, mainly in September and October. Few cases were reported in 2013-2018, and only six were imported from 2020 to 2022 due to the COVID-19 lockdowns. Our findings suggest that dengue prevention in Chongqing should focus on domestic and overseas population mobility, especially in the Yubei and Wanzhou districts, where airports and railway stations are located, and the period between August and October when dengue outbreaks occur in endemic regions. Moreover, continuous vector monitoring should be implemented, especially during August-October, which would be useful for controlling the Aedes mosquitoes. This study is significant for defining Chongqing's appropriate dengue prevention and control strategies.

Impact of climate change on dengue fever epidemics in South and Southeast Asian settings: A modelling study

The potential for dengue fever epidemic due to climate change remains uncertain in tropical areas. This study aims to assess the impact of climate change on dengue fever transmission in four South and Southeast Asian settings. We collected weekly data of dengue fever incidence, daily mean temperature and rainfall from 2012 to 2020 in Singapore, Colombo, Selangor, and Chiang Mai. Projections for temperature and rainfall were drawn for three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585) scenarios. Using a disease transmission model, we projected the dengue fever epidemics until 2090s and determined the changes in annual peak incidence, peak time, epidemic size, and outbreak duration. A total of 684,639 dengue fever cases were reported in the four locations between 2012 and 2020. The projected change in dengue fever transmission would be most significant under the SSP585 scenario. In comparison to the 2030s, the peak incidence would rise by 1.29 times in Singapore, 2.25 times in Colombo, 1.36 times in Selangor, and >10 times in Chiang Mai in the 2090s under SSP585. Additionally, the peak time was projected to be earlier in Singapore, Colombo, and Selangor, but be later in Chiang Mai under the SSP585 scenario. Even in a milder emission scenario of SSP126, the epidemic size was projected to increase by 5.94%, 10.81%, 12.95%, and 69.60% from the 2030s-2090s in Singapore, Colombo, Selangor, and Chiang Mai, respectively. The outbreak durations in the four settings were projected to be prolonged over this century under SSP126 and SSP245, while a slight decrease is expected in 2090s under SSP585. The results indicate that climate change is expected to increase the risk of dengue fever transmission in tropical areas of South and Southeast Asia. Limiting greenhouse gas emissions could be crucial in reducing the transmission of dengue fever in the future.

Projecting future risk of dengue related to hydrometeorological conditions in mainland China under climate change scenarios: a modelling study

BACKGROUND

We have limited knowledge on the impact of hydrometeorological conditions on dengue incidence in China and its associated disease burden in a future with a changed climate. This study projects the excess risk of dengue caused by climate change-induced hydrometeorological conditions across mainland China.

METHODS

In this modelling study, the historical association between the Palmer drought severity index (PDSI) and dengue was estimated with a spatiotemporal Bayesian hierarchical model from 70 cities. The association combined with the dengue-transmission biological model was used to project the annual excess risk of dengue related to PDSI by 2100 across mainland China, under three representative concentration pathways ([RCP] 2·6, RCP 4·5, and RCP 8·5).

FINDINGS

93 101 dengue cases were reported between 2013 and 2019 in mainland China. Dry and wet conditions within 3 months lag were associated with increased risk of dengue. Locations with potential dengue risk in China will expand in the future. The hydrometeorological changes are projected to substantially affect the risk of dengue in regions with mid-to-low latitudes, especially the coastal areas under high emission scenarios. By 2100, the annual average increased excess risk is expected to range from 12·56% (95% empirical CI 9·54-22·24) in northwest China to 173·62% (153·15-254·82) in south China under the highest emission scenario.

INTERPRETATION

Hydrometeorological conditions are predicted to increase the risk of dengue in the future in the south, east, and central areas of mainland China in disproportionate patterns. Our findings have implications for the preparation of public health interventions to minimise the health hazards of non-optimal hydrometeorological conditions in a context of climate change.

FUNDING

National Natural Science Foundation of China.

A systematic review of dengue outbreak prediction models: Current scenario and future directions

Dengue is among the fastest-spreading vector-borne infectious disease, with outbreaks often overwhelm the health system and result in huge morbidity and mortality in its endemic populations in the absence of an efficient warning system. A large number of prediction models are currently in use globally. As such, this study aimed to systematically review the published literature that used quantitative models to predict dengue outbreaks and provide insights about the current practices. A systematic search was undertaken, using the Ovid MEDLINE, EMBASE, Scopus and Web of Science databases for published citations, without time or geographical restrictions. Study selection, data extraction and management process were devised in accordance with the 'Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies' ('CHARMS') framework. A total of 99 models were included in the review from 64 studies. Most models sourced climate (94.7%) and climate change (77.8%) data from agency reports and only 59.6% of the models adjusted for reporting time lag. All included models used climate predictors; 70.7% of them were built with only climate factors. Climate factors were used in combination with climate change factors (13.4%), both climate change and demographic factors (3.1%), vector factors (6.3%), and demographic factors (5.2%). Machine learning techniques were used for 39.4% of the models. Of these, random forest (15.4%), neural networks (23.1%) and ensemble models (10.3%) were notable. Among the statistical (60.6%) models, linear regression (18.3%), Poisson regression (18.3%), generalized additive models (16.7%) and time series/autoregressive models (26.7%) were notable. Around 20.2% of the models reported no validation at all and only 5.2% reported external validation. The reporting of methodology and model performance measures were inadequate in many of the existing prediction models. This review collates plausible predictors and methodological approaches, which will contribute to robust modelling in diverse settings and populations.

Influence of hydroclimatic variability on dengue incidence in a tropical dryland area

Dengue is an endemic disease in more than 100 countries, but there are few studies about the effects of hydroclimatic variability on dengue incidence (DI) in tropical dryland areas. This study investigates the association between hydroclimatic variability and DI (2008-2018) in a large tropical dryland area. The area studied comprehends seven municipalities with populations ranging from 32,879 to 2,545,419 inhabitants. First, the precipitation and temperature impacts on interannual and seasonal DI were investigated. Then, the monthly association between DI and hydroclimatic variables was analyzed using generalized least squares (GLS) regression. The model's capability to reproduce DI given the current hydroclimatic conditions and DI seasonality over the entire time period studied were assessed. No association between the interannual variation of precipitation and DI was found. However, seasonal variation of DI was shaped by precipitation and temperature. February-July was the main dengue season period. A precipitation threshold, usually above 100 mm, triggers the rapid DI rising. Precipitation and minimum air temperature were the main explanatory variables. A two-month-lagged predictor was relevant for modeling, occurring in all regressions, followed by a non-lagged predictor. The climate predictors differed among the regression models, revealing the high spatial DI variability driven by hydroclimatic variability. GLS regressions were able to reproduce the beginning, development, and end of the dengue season, although we found underestimation of DI peaks and overestimation of low DI. These model limitations are not an issue for climate change impact assessment on DI at the municipality scale since historical DI seasonality was well simulated. However, they may not allow seasonal DI forecasting for some municipalities. These findings may help not only public health policies in the studied municipalities but also have the potential to be reproducible for other dryland regions with similar data availability.

Prediction of dengue fever outbreaks using climate variability and Markov chain Monte Carlo techniques in a stochastic susceptible-infected-removed model

The recent increase in the global incidence of dengue fever resulted in over 2.7 million cases in Latin America and many cases in Southeast Asia and has warranted the development and application of early warning systems (EWS) for futuristic outbreak prediction. EWS pertaining to dengue outbreaks is imperative; given the fact that dengue is linked to environmental factors owing to its dominance in the tropics. Prediction is an integral part of EWS, which is dependent on several factors, in particular, climate, geography, and environmental factors. In this study, we explore the role of increased susceptibility to a DENV serotype and climate variability in developing novel predictive models by analyzing RT-PCR and DENV-IgM confirmed cases in Singapore and Honduras, which reported high dengue incidence in 2019 and 2020, respectively. A random-sampling-based susceptible-infected-removed (SIR) model was used to obtain estimates of the susceptible fraction for modeling the dengue epidemic, in addition to the Bayesian Markov Chain Monte Carlo (MCMC) technique that was used to fit the model to Singapore and Honduras case report data from 2012 to 2020. Regression techniques were used to implement climate variability in two methods: a climate-based model, based on individual climate variables, and a seasonal model, based on trigonometrically varying transmission rates. The seasonal model accounted for 98.5% and 92.8% of the variance in case count in the 2020 Singapore and 2019 Honduras outbreaks, respectively. The climate model accounted for 75.3% and 68.3% of the variance in Singapore and Honduras outbreaks respectively, besides accounting for 75.4% of the variance in the major 2013 Singapore outbreak, 71.5% of the variance in the 2019 Singapore outbreak, and over 70% of the variance in 2015 and 2016 Honduras outbreaks. The seasonal model accounted for 14.2% and 83.1% of the variance in the 2013 and 2019 Singapore outbreaks, respectively, in addition to 91% and 59.5% of the variance in the 2015 and 2016 Honduras outbreaks, respectively. Autocorrelation lag tests showed that the climate model exhibited better prediction dynamics for Singapore outbreaks during the dry season from May to August and in the rainy season from June to October in Honduras. After incorporation of susceptible fractions, the seasonal model exhibited higher accuracy in predicting outbreaks of higher case magnitude, including those of the 2019–2020 dengue epidemic, in comparison to the climate model, which was more accurate in outbreaks of smaller magnitude. Such modeling studies could be further performed in various outbreaks, such as the ongoing COVID-19 pandemic to understand the outbreak dynamics and predict the occurrence of future outbreaks.

Modeling present and future climate risk of dengue outbreak, a case study in New Caledonia

BACKGROUND

Dengue dynamics result from the complex interactions between the virus, the host and the vector, all being under the influence of the environment. Several studies explored the link between weather and dengue dynamics and some investigated the impact of climate change on these dynamics. Most attempted to predict incidence rate at a country scale or assess the environmental suitability at a global or regional scale. Here, we propose a new approach which consists in modeling the risk of dengue outbreak at a local scale according to climate conditions and study the evolution of this risk taking climate change into account. We apply this approach in New Caledonia, where high quality data are available.

METHODS

We used a statistical estimation of the effective reproduction number (R_t) based on case counts to create a categorical target variable : epidemic week/non-epidemic week. A machine learning classifier has been trained using relevant climate indicators in order to estimate the probability for a week to be epidemic under current climate data and this probability was then estimated under climate change scenarios.

RESULTS

Weekly probability of dengue outbreak was best predicted with the number of days when maximal temperature exceeded 30.8°C and the mean of daily precipitation over 80 and 60 days prior to the predicted week respectively. According to scenario RCP8.5, climate will allow dengue outbreak every year in New Caledonia if the epidemiological and entomological contexts remain the same.

CONCLUSION

We identified locally relevant climatic factor driving dengue outbreaks in New Caledonia and assessed the inter-annual and seasonal risk of dengue outbreak under different climate change scenarios up to the year 2100. We introduced a new modeling approach to estimate the risk of dengue outbreak depending on climate conditions. This approach is easily reproducible in other countries provided that reliable epidemiological and climate data are available.

The impacts of precipitation patterns on dengue epidemics in Guangzhou city

Some studies have demonstrated that precipitation is an important risk factor of dengue epidemics. However, current studies mostly focused on a single precipitation variable, and few studies focused on the impact of precipitation patterns on dengue epidemics. This study aims to explore optimal precipitation patterns for dengue epidemics. Weekly dengue case counts and meteorological data from 2006 to 2018 in Guangzhou of China were collected. A generalized additive model with Poisson distribution was used to investigate the association between precipitation patterns and dengue. Precipitation patterns were defined as the combinations of three weekly precipitation variables: accumulative precipitation (Pre_A), the number of days with light or moderate precipitation (Pre_LMD), and the coefficient of precipitation variation (Pre_CV). We explored to identify optimal precipitation patterns for dengue epidemics. With a lead time of 10 weeks, minimum temperature, relative humidity, Pre_A, and Pre_LMD were positively associated with dengue, while Pre_CV was negatively associated with dengue. A precipitation pattern with Pre_A of 20.67-55.50 mm per week, Pre_LMD of 3-4 days per week, and Pre_CV less than 1.41 per week might be an optimal precipitation pattern for dengue epidemics in Guangzhou. The finding may be used for climate-smart early warning and decision-making of dengue prevention and control.

The Impact of Meteorological Factors on Communicable Disease Incidence and Its Projection: A Systematic Review

Background: Climate change poses a real challenge and has contributed to causing the emergence and re-emergence of many communicable diseases of public health importance. Here, we reviewed scientific studies on the relationship between meteorological factors and the occurrence of dengue, malaria, cholera, and leptospirosis, and synthesized the key findings on communicable disease projection in the event of global warming. Method: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flow checklist. Four databases (Web of Science, Ovid MEDLINE, Scopus, EBSCOhost) were searched for articles published from 2005 to 2020. The eligible articles were evaluated using a modified scale of a checklist designed for assessing the quality of ecological studies. Results: A total of 38 studies were included in the review. Precipitation and temperature were most frequently associated with the selected climate-sensitive communicable diseases. A climate change scenario simulation projected that dengue, malaria, and cholera incidence would increase based on regional climate responses. Conclusion: Precipitation and temperature are important meteorological factors that influence the incidence of climate-sensitive communicable diseases. Future studies need to consider more determinants affecting precipitation and temperature fluctuations for better simulation and prediction of the incidence of climate-sensitive communicable diseases.

Interaction of climate and socio-ecological environment drives the dengue outbreak in epidemic region of China

Transmission of dengue virus is a complex process with interactions between virus, mosquitoes and humans, influenced by multiple factors simultaneously. Studies have examined the impact of climate or socio-ecological factors on dengue, or only analyzed the individual effects of each single factor on dengue transmission. However, little research has addressed the interactive effects by multiple factors on dengue incidence. This study uses the geographical detector method to investigate the interactive effect of climate and socio-ecological factors on dengue incidence from two perspectives: over a long-time series and during outbreak periods; and surmised on the possibility of dengue outbreaks in the future. Results suggest that the temperature plays a dominant role in the long-time series of dengue transmission, while socio-ecological factors have great explanatory power for dengue outbreaks. The interactive effect of any two factors is greater than the impact of single factor on dengue transmission, and the interactions of pairs of climate and socio-ecological factors have more significant impact on dengue. Increasing temperature and surge in travel could cause dengue outbreaks in the future. Based on these results, three recommendations are offered regarding the prevention of dengue outbreaks: mitigating the urban heat island effect, adjusting the time and frequency of vector control intervention, and providing targeted health education to travelers at the border points. This study hopes to provide meaningful clues and a scientific basis for policymakers regarding effective interventions against dengue transmission, even during outbreaks.

Climate-based dengue model in Semarang, Indonesia: Predictions and descriptive analysis

Background

Dengue is one of the most rapidly spreading vector-borne diseases, which is considered to be a major health concern in tropical and sub-tropical countries. It is strongly believed that the spread and abundance of vectors are related to climate. Construction of climate-based mathematical model that integrates meteorological factors into disease infection model becomes compelling challenge since the climate is positively associated with both incidence and vector existence.

Methods

A host-vector model is constructed to simulate the dynamic of transmission. The infection rate parameter is replaced with the time-dependent coefficient obtained by optimization to approximate the daily dengue data. Further, the optimized infection rate is denoted as a function of climate variables using the Autoregressive Distributed Lag (ARDL) model.

Results

The infection parameter can be extended when updated daily climates are known, and it can be useful to forecast dengue incidence. This approach provides proper prediction, even when tested in increasing or decreasing prediction windows. In addition, associations between climate and dengue are presented as a reversed slide-shaped curve for dengue-humidity and a reversed U-shaped curves for dengue-temperature and dengue-precipitation. The range of optimal temperature for infection is 24.3–30.5 °C. Humidity and precipitation are positively associated with dengue upper the threshold 70% at lag 38 days and below 50 mm at lag 50 days, respectively.

Conclusion

Identification of association between climate and dengue is potentially useful to counter the high risk of dengue and strengthen the public health system and reduce the increase of the dengue burden.

Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents

Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases, such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28–85% for vectors, 44–88% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.

The effects of climate on vector-borne disease systems are highly context-dependent. Here, the authors incorporate laboratory-measured physiological traits of the mosquito Aedes aegypti into climate-driven mechanistic models to predict number, timing, and duration of outbreaks in Ecuador and Kenya.

Modeling and prediction of dengue occurrences in Kolkata, India, based on climate factors

Dengue is one of the most serious vector-borne infectious diseases in India, particularly in Kolkata and its neighbouring districts. Dengue viruses have infected several citizens of Kolkata since 2012 and it is amplifying every year. It has been derived from earlier studies that certain meteorological variables and climate change play a significant role in the spread and amplification of dengue infections in different parts of the globe. In this study, our primary objective is to identify the relative contribution of the putative drivers responsible for dengue occurrences in Kolkata and project dengue incidences with respect to the future climate change. The regression model was developed using maximum temperature, minimum temperature, relative humidity and rainfall as key meteorological factors on the basis of statistically significant cross-correlation coefficient values to predict dengue cases. Finally, climate variables from the Coordinated Regional Climate Downscaling Experiment (CORDEX) for South Asia region were input into the statistical model to project the occurrences of dengue infections under different climate scenarios such as Representative Concentration Pathways (RCP4.5 and RCP8.5). It has been estimated that from 2020 to 2100, dengue cases will be higher from September to November with more cases in RCP8.5 (872 cases per year) than RCP4.5 (531 cases per year). The present research further concludes that from December to February, RCP8.5 leads to suitable warmer weather conditions essential for the survival and multiplication of dengue pathogens resulting more than two times dengue cases in RCP8.5 than in RCP4.5. Furthermore, the results obtained will be useful in developing early warning systems and provide important evidence for dengue control policy-making and public health intervention.

Effects of natural and socioeconomic factors on dengue transmission in two cities of China from 2006 to 2017

Dengue fever (DF) is a common and rapidly spreading vector-borne viral disease in tropical and subtropical regions. In recent years, in China, DF still poses an increasing threat to public health in many cities; but the incidence shows significant spatiotemporal differences. The purpose of this study was to identify the key factors affecting the spatial and temporal distribution of DF. We collected natural environmental and socio-economic data for two adjacent cities, Guangzhou (73 variables) and Foshan (71 variables), with the most DF cases in China. We performed random forest modelling to rank the factors according to their level of importance, and used negative binomial regression analysis to compare the risk factors between outbreak years and non-outbreak years. The natural environmental factors contributing to DF incidence for Guangzhou were temperature (relative risk (RR) = 18.80, 95% confidence interval (CI) = 3.11-113.67), humidity (RR = 1.85, 95% CI = 1.17-2.90) and green area (RR = 12.11, 95% CI = 6.14-55.50), and for Foshan was forest coverage (RR = 5.83, 95% CI = 2.72-12.45). Socio-economic impact were shown in Guangzhou with foreign visitor (RR = 1.18, 95% CI = 1.05-1.34) and oversea air passenger transport (RR = 7.34, 95% CI = 2.26-23.86); in Foshan, with oversea tourism (RR = 1.65, 95% CI = 1.34-2.04); and in Guangzhou-Foshan, with the development of intercity metro (RR = 1.26, 95% CI = 1.10-1.44). The difference between the two cities was the greater impact of the foreign visitor, green spaces and climate factor on DF in Guangzhou; the impact of the construction of intercity metro; and the more significant impact of oversea tourism on DF in Foshan. Our results suggest meaningful clues to public health authorities implementing joint interventions on DF prevention and early warning, to increase health education on DF prevention for international visitors and oversea travelers, and cross-city metro passengers; using rapid body temperature detection in public places such as airports, metros and parks can help detect cases early.

Impact of climate change on dysentery: Scientific evidences, uncertainty, modeling and projections

Dysentery is water-borne and food-borne infectious disease and its incidence is sensitive to climate change. Although the impact of climate change on dysentery is being studied in specific areas, a systematic review is lacking. We searched the worldwide literature using three sets of keywords and six databases. We identified and selected 98 studies during 1866-2019 and reviewed the relevant findings. Climate change, including long-term variations in factors, such as temperature, precipitation, and humidity, and short-term variations in extreme weather events, such as floods and drought, mostly had a harmful impact on dysentery incidence. However, some uncertainty over the exact effects of climate factors exists, specifically in the different indexes for the same climate factor, various determinant indexes for different dysentery burdens, and divergent effects for different population groups. These complicate the accurate quantification of such impacts. We generalized two types of methods: sensitivity analysis, used to detect the sensitivity of dysentery to climate change, including Pearson's and Spearman's correlations; and mathematical models, which quantify the impact of climate on dysentery, and include models that examine the associations (including negative binomial regression models) and quantify correlations (including single generalized additive models and mixed models). Projection studies mostly predict disease risks, and some predict disease incidence based on climate models under RCP 4.5. Since some geographic heterogeneity exists in the climate-dysentery relationship, modeling and projection of dysentery incidence on a national or global scale remain challenging. The reviewed results have implications for the present and future. Current research should be extended to select appropriate and robust climate-dysentery models, reasonable disease burden measure, and appropriate climate models and scenarios. We recommend future studies focus on qualitative investigation of the mechanism involved in the impact of climate on dysentery, and accurate projection of dysentery incidence, aided by advancing accuracy of extreme weather forecasting.

Projecting the future of dengue under climate change scenarios: Progress, uncertainties and research needs

BACKGROUND

Dengue is a mosquito-borne viral disease and its transmission is closely linked to climate. We aimed to review available information on the projection of dengue in the future under climate change scenarios.

METHODS

Using five databases (PubMed, ProQuest, ScienceDirect, Scopus and Web of Science), a systematic review was conducted to retrieve all articles from database inception to 30th June 2019 which projected the future of dengue under climate change scenarios. In this review, "the future of dengue" refers to disease burden of dengue, epidemic potential of dengue cases, geographical distribution of dengue cases, and population exposed to climatically suitable areas of dengue.

RESULTS

Sixteen studies fulfilled the inclusion criteria, and five of them projected a global dengue future. Most studies reported an increase in disease burden, a wider spatial distribution of dengue cases or more people exposed to climatically suitable areas of dengue as climate change proceeds. The years 1961-1990 and 2050 were the most commonly used baseline and projection periods, respectively. Multiple climate change scenarios introduced by the Intergovernmental Panel on Climate Change (IPCC), including B1, A1B, and A2, as well as Representative Concentration Pathway 2.6 (RCP2.6), RCP4.5, RCP6.0 and RCP8.5, were most widely employed. Instead of projecting the future number of dengue cases, there is a growing consensus on using "population exposed to climatically suitable areas for dengue" or "epidemic potential of dengue cases" as the outcome variable. Future studies exploring non-climatic drivers which determine the presence/absence of dengue vectors, and identifying the pivotal factors triggering the transmission of dengue in those climatically suitable areas would help yield a more accurate projection for dengue in the future.

CONCLUSIONS

Projecting the future of dengue requires a systematic consideration of assumptions and uncertainties, which will facilitate the development of tailored climate change adaptation strategies to manage dengue.

The association between dengue incidences and provincial-level weather variables in Thailand from 2001 to 2014

Dengue and dengue hemorrhagic pose significant burdens in many tropical countries. Dengue incidences have perpetually increased, leading to an annual (uncertain) peak. Dengue cases cause an enormous public health problem in Thailand because there is no anti-viral drug against the dengue virus. Searching for means to reduce the dengue incidences is a challenging and appropriate strategy for primary prevention in a dengue outbreak. This study constructs the best predictive model from past statistical dengue incidences at the provincial level and studies the relationships among dengue incidences and weather variables. We conducted experiments for 65 provinces (out of 77 provinces) in Thailand since there is no dengue information for the remaining provinces. Predictive models were constructed using weekly data during 2001-2014. The training set are data during 2001-2013, and the test set is the data from 2014. Collected data were separated into two parts: current dengue cases as the dependent variable, and weather variables and previous dengue cases as the independent variables. Eight weather variables are used in our models: average pressure, maximum temperature, minimum temperature, average humidity, precipitation, vaporization, wind direction, wind power. Each weather variable includes the current week and one to three weeks of lag time. A total of 32 independent weather variables are used for each province. The previous one to three weeks of dengue cases are also used as independent variables. There is a total of 35 independent variables. Predictive models were constructed using five methods: Poisson regression, negative binomial regression, quasi-likelihood regression, ARIMA(3,1,4) and SARIMA(2,0,1)(0,2,0). The best model is determined by combinations of 1–12 variables, which are 232,989,800 models for each province. We construct a total of 15,144,337,000 models. The best model is selected by the average from high to low of the coefficient of determination (R2) and the lowest root mean square error (RMSE). From our results, the one-week lag previous case variable is the most frequent in 55 provinces out of a total of 65 provinces (coefficient of determinations with a minimum of 0.257 and a maximum of 0.954, average of 0.6383, 95% CI: 0.57313 to 0.70355). The most influential weather variable is precipitation, which is used in most of the provinces, followed by wind direction, wind power, and barometric pressure. The results confirm the common knowledge that dengue incidences occur most often during the rainy season. It also shows that wind direction, wind power, and barometric pressure also have influences on the number of dengue cases. These three weather variables may help adult mosquitos to survive longer and spread dengue. In conclusion, The most influential factor for further cases is the number of dengue cases. However, weather variables are also needed to obtain better results. Predictions of the number of dengue cases should be done locally, not at the national level. The best models of different provinces use different sets of weather variables. Our model has an accuracy that is sufficient for the real prediction of future dengue incidences, to prepare for and protect against severe dengue outbreaks.

Analysis of influencing factors on soil Zn content using generalized additive model

Soil zinc (Zn) plays a crucial role in plant growth, but excessive accumulation in the environment may lead to air, water and soil pollution. It is affected by various chemical, environmental and spatial factors. Therefore, it is important to identify the factors influencing Zn content in the landscape. The main motivation for this study is to determine the suitability of a generalized additive model (GAM) to describe change in soil Zn content due to influencing factors. A total of 1497 soil nutrient samples were collected in Fangshan District, Beijing, China. Organic matter (OM), available phosphorus (AP), available potassium (AK), alkali-hydrolyzed nitrogen (AHN) and slowly available potassium (SAK) are considered. The relationship between Zn, nutrients and geographic location (latitude & longitude) is investigated using the GAM. More precisely, the Akaike information criterion (AIC) is used to select influencing factors on Zn content and cross-validated to avoid overfitting of the multivariate model. The results show that Zn content reaches its maximum at latitude 39.8°N and longitude 115.9°E. Zinc content increases as AP increases to 150 mg/kg. When OM content is greater than 90 g/kg, Zinc content decreases with an increase in OM content. Factors that affected Zn content, in descending order of significance derived from deviance explained and adjustment coefficient of determination (Adj.R²) were AP, latitude, AHN, AK and OM. Moreover, the interactions between latitude and longitude, AHN and AP, OM and AK have significant impact on Zn.

Climate change and dengue fever transmission in China: Evidences and challenges

Dengue Fever (DF) has become one of the most serious infectious diseases in China. Dengue virus and its vector (Aedes mosquito) are known to be sensitive to climate condition. Climate impacts DF through affecting three essential bioecological aspects: DF virus, vector (mosquito) and DF transmission environment. Weather-based DF model, mosquito model and climate model are the three pillars to help the prediction of DF distribution. Through a systematic review of literature between 1980 and 2017, this paper summarizes empirical evidences in China on the impact of climate change on DF; it further reviews the related DF incidence models and their findings on how changes in weather factors may impact DF occurrences in China. Compared with some well-known research projects in the western countries, there is a lack of knowledge in China regarding how the spatiotemporal distribution of DF will respond to climate change. However, being able to predict DF distribution is key to China's efforts to prevent and control DF transmission. We conclude this paper by recommending four focused areas for China: promoting more advanced research on the relationship between extreme weather events and DF, developing regional-specific models for the high risk regions of DF in south China, encouraging interdisciplinary collaboration between climate studies and health services, and enhancing public health education and management at national, regional and local levels.

Meteorological factors and the incidence of mumps in Fujian Province, China, 2005-2013: Non-linear effects

BACKGROUND

Mumps is still an important public health issue in the world with several recent outbreaks. The seasonable distribution of the disease suggested that meteorological factors may influence the incidence of mumps. The aim of this study was to explore the possible association between meteorological factors and the incidence of mumps, and to provide scientific evidence to relevant health authorities for the disease control and prevention.

METHODS

We obtained the data of mumps cases and daily meteorological factors in Fujian Province in Eastern China over the period of 2005-2013. Using distributed lag non-linear model (DLNM) approach, we assessed the relationship between the meteorological factors and mumps incidence.

RESULTS

The effects of meteorological factors on the mumps incidence were all non-linear. Compared with the lowest risk values, the upper level of precipitation, atmospheric pressure and relative humidity could increase the risk of mumps, whereas the low level of wind velocity, temperature, diurnal temperature range and sunshine duration may also increase the risk. Moderate atmospheric pressure and low wind velocity had larger cumulative effects within 30lagdays and the relative risks were 10.02 (95%CI: 2.47-40.71) and 12.45 (95%CI: 1.40-110.78). For temperature, the cumulative effect within 30lagdays of minimum temperature was higher than that from maximum temperature in most populations. The cumulative effects of minimum temperature for males, children aged 10-14 and students were higher than those in other populations.

CONCLUSIONS

Meteorological factors, especially temperature and wind velocity, should be taken into consideration in the prevention and warning of possible mumps epidemic. Special attention should be paid to the vulnerable populations, such as teenagers and young adults.