Analysis of climate factors and dengue incidence in the metropolitan region of Rio de Janeiro, Brazil

Assessing dengue forecasting methods: a comparative study of statistical models and machine learning techniques in Rio de Janeiro, Brazil

BACKGROUND

Dengue is a mosquito-borne viral disease that poses a significant public health threat in tropical and subtropical regions worldwide. Accurate forecasting of dengue outbreaks is crucial for effective public health planning and intervention. This study aims to assess the predictive performance and computational efficiency of a number of statistical models and machine learning techniques for dengue forecasting, both with and without the inclusion of climate factors, to inform the design of dengue surveillance systems.

METHODS

The dengue forecasting methods comparison in this study considers dengue cases in Rio de Janeiro, Brazil, as well as climate factors known to affect disease transmission. Employing a dynamic window approach, various statistical methods and machine learning techniques were used to generate weekly forecasts at several time horizons. Error measures, uncertainty intervals, and computational efficiency obtained with each method were compared. Statistical models considered were Autoregressive (AR), Moving Average (MA), Autoregressive Integrated Moving Average (ARIMA), and Exponential Smoothing State Space Model (ETS). In addition, models incorporating temperature and humidity as covariates, such as Vector Autoregression (VAR) and Seasonal ARIMAX (SARIMAX), were employed. Machine learning techniques evaluated were Random Forest, XGBoost, Support Vector Machine (SVM), Long-Short-Term Memory (LSTM) networks, and Prophet. Ensemble approaches that integrated the top performing methods were also considered. The evaluated methods also incorporated lagged climatic variables to account for delayed effects.

RESULTS

Among the statistical models, ARIMA demonstrated the best performance using only historical case data, while SARIMAX significantly improved predictive accuracy by incorporating climate covariates. In general, the LSTM model, particularly when combined with climate covariates, proved to be the most accurate machine learning model, despite being slower to train and predict. For long-term forecasts, Prophet with climate covariates was the most effective. Ensemble models, such as the combination of LSTM and ARIMA, showed substantial improvements over individual models.

CONCLUSIONS

This study demonstrates the strengths and limitations of various methods for dengue forecasting across multiple timeframes. It highlights the best-performing statistical and machine learning methods, including their computational efficiency, underscoring the significance of machine learning techniques and the integration of climate covariates to improve forecasts. These findings offer valuable insights for public health officials, facilitating the development of dengue surveillance systems for more accurate forecasting and timely allocation of resources to mitigate dengue outbreaks.

The Ecological, Biological, and Social Determinants of Dengue Epidemiology in Latin America and the Caribbean: A Scoping Review of the Literature

Dengue has re-emerged in Latin America and the Caribbean (LAC) over the last five decades. The factors influencing dengue transmission by the Aedes aegypti mosquito vector within the region can be classified as ecological, biological, and social determinants. In this review, we summarized the published literature on the evidence for the determinants of dengue vector dynamics, transmission, and epidemiological outcomes in LAC. We searched PubMed, SCOPUS, and LILACS databases in September 2022 to collect published works irrespective of study design published in either English, French, Portuguese, or Spanish. Full-text articles were obtained for the studies that passed the title and abstract screening process. During full-text screening, articles were assessed to determine if they met the eligibility criteria. Data were extracted using NVivo™ 12. Data were organized as NVivo codes. Themes were compiled and communicated narratively. We included 90 peer-reviewed research articles from LAC between 2007 and 2022. The included studies were from 15 different countries, dependencies, and territories in the region. Several dengue-related indicators and outcomes were classified as ecological, biological, or social. Eight main factors were found, including: micro- and macro-climatic factors; entomological and pathogenic factors; and global-, community-, household-, and individual- level social factors. We identified several existing knowledge gaps in the literature. Making salient these gaps may serve as a starting point for future vector-borne infectious disease research to equip policymakers and public health practitioners to develop effective strategies to mitigate the impact of dengue and protect vulnerable populations in LAC.

Disentangling climate's dual role in dengue dynamics: A multiregion causal analysis study

Dengue fever poses major public health challenges, with climate change complicating control efforts. Yet, the full extent of climate change's impact on dengue remains elusive. To investigate this, we used an advanced causal inference method to 16 diverse climatic regions in the Philippines. This method is capable of detecting nonlinear and joint effects of temperature and rainfall to dengue incidence. We found that temperature consistently increased dengue incidence throughout all the regions, while rainfall effects differed depending on the variation in dry season length, a factor previously overlooked. Specifically, our results showed that regions with low variation in dry season length experience a negative impact of rainfall on dengue incidence likely due to strong flushing effect on mosquito habitats, while regions with high variation in dry season length experience a positive impact, likely due to increased mosquito breeding sites. Our findings emphasize the need for tailored prevention strategies based on local climate conditions, rather than a one-size-fits-all approach.

Detecting Arboviruses Through Screening Asymptomatic Blood Donors in Rio de Janeiro/Brazil During a Dengue Outbreak

Arthropod-borne viruses (arboviruses) dengue (DENV), chikungunya (CHIK), and Zika (ZIKV) have been responsible for a high number of outbreaks worldwide. However, their screening in blood donors is not mandatory, and asymptomatic cases might act as an important cause of virus transmission via transfusion. A study was conducted to assess the presence of DENV (serotypes 1-4), ZIKV, and CHIKV in pooled samples (pool size: six) from asymptomatic blood donors. A total of 9463 plasma pools, corresponding to 56,778 blood donations from asymptomatic blood donors who attended donor sessions at HEMORIO and other blood centers in Rio de Janeiro and Espírito Santo, was submitted to automated nucleic-acid extraction and PCR amplification using ZC D-Tipagem molecular assay (Bio-Manguinhos). In general, a pool prevalence of 1% (95/9463) and a donor prevalence of 0.17% (95/56,778) were observed. January and February 2024 had a total of 62 positive pools out of 95 (65.3%). Targets DENV-1 and -2 had a higher prevalence in the studied months-early summer-with 24 and 28 positive pools, respectively. ZC D-Tipagem molecular assay was able to detect the best-known arboviruses circulating in asymptomatic blood donors; this study suggested that ZIKV, CHIK, and DENV are circulating in asymptomatic blood donors before blood donations and can be transmitted to blood transfusion recipients.

Climate change impacts on dengue transmission areas in Espírito Santo state, Brazil

Espírito Santo state, in Brazil, is a dengue-endemic region predicted to suffer from an increase in temperature and drought due to climate change, which could affect the areas with active dengue virus transmission. The study objective was modeling climatic factors and climate change effects in zones suitable for dengue virus transmission in Espírito Santo state, Brazil. Data on dengue reports from 2022 were used to determine climatic variables related to spatial distribution. The climate change projections were generated for the 2030s, 2050s, 2070s, and 2090s for three distinct Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5 and SSP5-8.5. A maximum entropy algorithm was used to construct the three models and projections, and the results were used to calculate the ensemble mean. Isothermality, the maximum temperature of the warmest month, precipitation of the wettest month, precipitation of the warmest quarter, and annual precipitation impacted the model. Projections indicated a change in areas suitable for dengue virus transmission, varying from -30.44% in the 2070s (SSP1-2.6) to +13.07% in the 2070s (SSP5-8.5) compared to 2022. The coastal regions were consistently suitable in all scenarios. Urbanized and highly populated areas were predicted to persist with active dengue transmission in Espírito Santo state, posing challenges for public health response.

Mathematical analysis and prediction of future outbreak of dengue on time-varying contact rate using machine learning approach

This article introduces a novel mathematical model analyzing the dynamics of Dengue in the recent past, specifically focusing on the 2023 outbreak of this disease. The model explores the patterns and behaviors of dengue fever in Bangladesh. Incorporating a sinusoidal function reveals significant mid-May to Late October outbreak predictions, aligning with the government's exposed data in our simulation. For different amplitudes (A) within a sequence of values (A = 0.1 to 0.5), the highest number of infected mosquitoes occurs in July. However, simulations project that when βM = 0.5 and A = 0.1, the peak of human infections occurs in late September. Not only the next-generation matrix approach along with the stability of disease-free and endemic equilibrium points are observed, but also a cutting-edge Machine learning (ML) approach such as the Prophet model is explored for forecasting future Dengue outbreaks in Bangladesh. Remarkably, we have fitted our solution curve of infection with the reported data by the government of Bangladesh. We can predict the outcome of 2024 based on the ML Prophet model situation of Dengue will be detrimental and proliferate 25 % compared to 2023. Finally, the study marks a significant milestone in understanding and managing Dengue outbreaks in Bangladesh.

Serotype-specific clinical features and spatial distribution of dengue in northern Kerala, India

Background

Collection and compilation of spatial, meteorological, entomological, and virological data are critical in mitigating climate-sensitive emerging infections like dengue. This study was a holistic attempt to understand the dengue situation in the Kasaragod district of Kerala, India.

Methods

This cross-sectional study was conducted in 13 health institutions from June to July 2021. Adult patients presenting with fever and testing positive for NS1 ELISA were subjected to Dengue RT-PCR and serotyping. The spatial and clinical features of the RT-PCR-positive patients, the district's meteorological data, and the vector indices were studied.

Results

The pre-epidemic months were marked by intermittent rainfall, peak ambient temperature and high larval indices. Among the 136 dengue RT-PCR patients studied, 41.2% had DENV2 followed by DENV1 (22.8%), DENV3 (5.9%) and DENV4 (4.4%); with 25% mixed infections. DENV1 showed a higher risk of gastrointestinal manifestations (80.6%, p=0.019) and musculoskeletal symptoms (77.4%, p=0.026) compared with other serotypes.

Conclusions

In the context of dengue hyperendemicity, the possibility of an emerging serotype's dominance coupled with the mixing up of strains should warn the health system regarding future outbreaks. Furthermore, the study emphasizes the importance of monitoring larval indices and the window of opportunity to intervene between environmental predictors and dengue outbreaks.

Societal knowledge, attitude, and practices towards dengue and associated factors in epidemic-hit areas: Geoinformation assisted empirical evidence

Dengue is one of Pakistan's major health concerns. In this study, we aimed to advance our understanding of the levels of knowledge, attitudes, and practices (KAPs) in Pakistan's Dengue Fever (DF) hotspots. Initially, at-risk communities were systematically identified via a well-known spatial modeling technique, named, Kernel Density Estimation, which was later targeted for a household-based cross-sectional survey of KAPs. To collect data on sociodemographic and KAPs, random sampling was utilized (n = 385, 5 % margin of error). Later, the association of different demographics (characteristics), knowledge, and attitude factors-potentially related to poor preventive practices was assessed using bivariate (individual) and multivariable (model) logistic regression analyses. Most respondents (>90 %) identified fever as a sign of DF; headache (73.8 %), joint pain (64.4 %), muscular pain (50.9 %), pain behind the eyes (41.8 %), bleeding (34.3 %), and skin rash (36.1 %) were identified relatively less. Regression results showed significant associations of poor knowledge/attitude with poor preventive practices; dengue vector (odds ratio [OR] = 3.733, 95 % confidence interval [CI ] = 2.377-5.861; P < 0.001), DF symptoms (OR = 3.088, 95 % CI = 1.949-4.894; P < 0.001), dengue transmission (OR = 1.933, 95 % CI = 1.265-2.956; P = 0.002), and attitude (OR = 3.813, 95 % CI = 1.548-9.395; P = 0.004). Moreover, education level was stronger in bivariate analysis and the strongest independent factor of poor preventive practices in multivariable analysis (illiterate: adjusted OR = 6.833, 95 % CI = 2.979-15.672; P < 0.001) and primary education (adjusted OR = 4.046, 95 % CI = 1.997-8.199; P < 0.001). This situation highlights knowledge gaps within urban communities, particularly in understanding dengue transmission and signs/symptoms. The level of education in urban communities also plays a substantial role in dengue control, as observed in this study, where poor preventive practices were more prevalent among illiterate and less educated respondents.

Association of climate factors with dengue incidence in Bangladesh, Dhaka City: A count regression approach

Background

In Bangladesh, particularly in Dhaka city, dengue fever is a major factor in serious sickness and hospitalization. The weather influences the temporal and geographical spread of the vector-borne disease dengue in Dhaka. As a result, rainfall and ambient temperature are considered macro factors influencing dengue since they have a direct impact on Aedes aegypti population density, which changes seasonally dependent on these critical variables. This study aimed to clarify the relationship between climatic variables and the incidence of dengue disease.

Methods

A total of 2253 dengue and climate data were used for this study. Maximum and minimum temperature (°C), humidity (grams of water vapor per kilogram of air g.kg^-1), rainfall (mm), sunshine hour (in (average) hours per day), and wind speed (knots (kt)) in Dhaka were considered as the independent variables for this study which trigger the dengue incidence in Dhaka city, Bangladesh. Missing values were imputed using multiple imputation techniques. Descriptive and correlation analyses were performed for each variable and stationary tests were observed using Dicky Fuller test. However, initially, the Poisson model, zero-inflated regression model, and negative binomial model were fitted for this problem. Finally, the negative binomial model is considered the final model for this study based on minimum AIC values.

Results

The mean of maximum and minimum temperature, wind speed, sunshine hour, and rainfall showed some fluctuations over the years. However, a mean number of dengue cases reported a higher incidence in recent years. Maximum and minimum temperature, humidity, and wind speed were positively correlated with dengue cases. However, rainfall and sunshine hours were negatively associated with dengue cases. The findings showed that factors such as maximum temperature, minimum temperature, humidity, and windspeed are crucial in the transmission cycles of dengue disease. On the other hand, dengue cases decreased with higher levels of rainfall.

Conclusion

The findings of this study will be helpful for policymakers to develop a climate-based warning system in Bangladesh.

Dengue as a Disease Threatening Global Health: A Narrative Review Focusing on Latin America and Brazil

Arboviruses constitute the largest known group of viruses. These viruses are the etiological agents of pathologies known as arboviruses, with dengue being one of the most prevalent. Dengue has resulted in important socioeconomic burdens placed on different countries around the world, including those in Latin America, especially Brazil. Thus, this work intends to carry out a narrative-based review of the literature, conducted using a study of the secondary data developed through a survey of scientific literature databases, and to present the situation of dengue, particularly its distribution in these localities. Our findings from the literature demonstrate the difficulties that managers face in controlling the spread of and planning a response against dengue, pointing to the high cost of the disease for public coffers, rendering the resources that are already limited even scarcer. This can be associated with the different factors that affect the spread of the disease, including ecological, environmental, and social factors. Thus, in order to combat the disease, it is expected that targeted and properly coordinated public policies need to be adopted not only in specific localities, but also globally.

Prevalence of Dengue, Chikungunya and Zika Viruses in Blood Donors in the State of Pará, Northern Brazil: 2018-2020

Arboviruses have been reported over the years as constant threats to blood transfusion recipients, given the high occurrence of asymptomatic cases and the fact that the presence of viremia precedes the onset of symptoms, making it possible that infected blood from donors act as a source of dissemination. This work aims to identify the prevalence of dengue virus (DENV), Zika virus (ZIKV) and Chikungunya virus (CHIKV) infection in blood donors during epidemic and non-epidemic periods; classify the donor as symptomatic or asymptomatic; and verify the need to include DENV, CHIKV and ZIKV in the nucleic acid test (NAT) platform in northern Brazil. We investigated 36,133 thousand donations in two years of collection in Northern Brazil. One donor was positive for DENV and one for CHIKV (0.002% prevalence). As the prevalence for arboviruses was low in this study, it would not justify the individual screening of samples from donors in a blood bank. Thus, DENV- and CHIKV-positive samples were simulated in different amounts of sample pools, and both were safely detected by molecular biology even in a pool of 14 samples, which would meet the need to include these three viruses in the routine of blood centers in endemic countries such as Brazil.

Adaptation of a Human Diagnostic Kit to Detect Dengue, Zika, and Chikungunya Viruses in Mosquito Samples (Aedes aegypti and Aedes albopictus): A Contribution to Public Health in the International Triple Border (Brazil, Paraguay, and Argentina)

Objective: The objective of this work was to adapt a diagnostic kit developed for humans to identify Dengue (DENV1, DENV2, DENV3, DENV4), Zika (ZIKV) and Chikungunya virus (CHIKV) in females of Aedes aegypti and Aedes albopictus and to verify if the occurrence of mosquitoes infected with these three arboviruses are being found in regions with high occurrence of these diseases in humans. Materials and Methods: For this purpose, live mosquitoes were captured between January and June 2020 using 3,476 traps permanently installed in the field were used. After capture, the species were identified, then the females were placed in a pool of 2 to 10 specimens and sent to the laboratory for detection of DENV1, DENV2, DENV3, DENV4, ZIKV and CHIKV by RT-PCR using a commercial human kit for arboviruses. Results: Of the 76 mosquito pools collected, six (7.9%) pools tested positive for the DENV2 virus. The DENV-positive mosquitoes were collected in regions with a high incidence of reported cases of Dengue or in adjacent areas. Conclusion: The absence of kits for the detection of these arboviruses in Aedes is a limiting factor and the adequacy of commercial kits, already used for the diagnosis of arboviruses in humans, the results presented demonstrate that it is possible to identify the presence of DENV2 in mosquitoes with the respective kit, reinforcing the use of RT-qPCR as a robust diagnostic tool for epidemiological surveillance allowing managers to receive timely results for decision-making regarding prevention and control actions.

Quantifying the future risk of dengue under climate change in Japan

Background

In metropolitan Tokyo in 2014, Japan experienced its first domestic dengue outbreak since 1945. The objective of the present study was to quantitatively assess the future risk of dengue in Japan using climate change scenarios in a high-resolution geospatial environment by building on a solid theory as a baseline in consideration of future adaptation strategies.

Methods

Using climate change scenarios of the Model for Interdisciplinary Research on Climate version 6 (MIROC6), representative concentration pathway (RCP) 2.6, 4.5, and 8.5, we computed the daily average temperature and embedded this in the effective reproduction number of dengue, R(T), to calculate the extinction probability and interepidemic period across Japan.

Results

In June and October, the R(T) with daily average temperature T, was <1 as in 2022; however, an elevation in temperature increased the number of days with R(T) >1 during these months under RCP8.5. The time period with a risk of dengue transmission gradually extended to late spring (April–May) and autumn (October–November). Under the RCP8.5 scenario in 2100, the possibility of no dengue-free months was revealed in part of southernmost Okinawa Prefecture, and the epidemic risk extended to the entire part of northernmost Hokkaido Prefecture.

Conclusion

Each locality in Japan must formulate action plans in response to the presented scenarios. Our geographic analysis can help local governments to develop adaptation policies that include mosquito breeding site elimination, distribution of adulticides and larvicides, and elevated situation awareness to prevent transmission via bites from Aedes vectors.

A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions

Climate change is unexpected weather patterns that can create an alarming situation. Due to climate change, various sectors are affected, and one of the sectors is healthcare. As a result of climate change, the geographic range of several vector-borne human infectious diseases will expand. Currently, dengue is taking its toll, and climate change is one of the key reasons contributing to the intensification of dengue disease transmission. The most important climatic factors linked to dengue transmission are temperature, rainfall, and relative humidity. The present study carries out a systematic literature review on the surveillance system to predict dengue outbreaks based on Machine Learning modeling techniques. The systematic literature review discusses the methodology and objectives, the number of studies carried out in different regions and periods, the association between climatic factors and the increase in positive dengue cases. This study also includes a detailed investigation of meteorological data, the dengue positive patient data, and the pre-processing techniques used for data cleaning. Furthermore, correlation techniques in several studies to determine the relationship between dengue incidence and meteorological parameters and machine learning models for predictive analysis are discussed. In the future direction for creating a dengue surveillance system, several research challenges and limitations of current work are discussed.

Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling

Simple Summary

Forecasting dengue cases often face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without historical dengue cases. With the advance of the geospatial big data cloud computing in Google Earth Engine and deep learning, this study proposed an efficient framework of dengue prediction at an epidemiological week basis using geospatial big data analysis in Google Earth Engine and Long Short Term Memory modeling. We focused on the dengue epidemics in the Federal District of Brazil during 2007–2019. Based on Google Earth Engine and epidemiological calendar, we computed the weekly composite for each dengue driving factor, and spatially aggregated the pixel values into dengue transmission areas to generate the time series of driving factors. A multi-step-ahead Long Short Term Memory modeling was used, and the time-differenced natural log-transformed dengue cases and the time series of driving factors were considered as outcomes and explantary factors, respectively, with two modeling scenarios (with and without historical cases). The performance is better when historical cases were used, and the 5-weeks-ahead forecast has the best performance.

Abstract

Timely and accurate forecasts of dengue cases are of great importance for guiding disease prevention strategies, but still face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation capability due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without the application of historical case information. Geospatial big data, cloud computing platforms (e.g., Google Earth Engine, GEE), and emerging deep learning algorithms (e.g., long short term memory, LSTM) provide new opportunities for advancing these efforts. Here, we focused on the dengue epidemics in the urban agglomeration of the Federal District of Brazil (FDB) during 2007–2019. A new framework was proposed using geospatial big data analysis in the Google Earth Engine (GEE) platform and long short term memory (LSTM) modeling for dengue case forecasts over an epidemiological week basis. We first defined a buffer zone around an impervious area as the main area of dengue transmission by considering the impervious area as a human-dominated area and used the maximum distance of the flight range of Aedes aegypti and Aedes albopictus as a buffer distance. Those zones were used as units for further attribution analyses of dengue epidemics by aggregating the pixel values into the zones. The near weekly composite of potential driving factors was generated in GEE using the epidemiological weeks during 2007–2019, from the relevant geospatial data with daily or sub-daily temporal resolution. A multi-step-ahead LSTM model was used, and the time-differenced natural log-transformed dengue cases were used as outcomes. Two modeling scenarios (with and without historical dengue cases) were set to examine the potential of historical information on dengue forecasts. The results indicate that the performance was better when historical dengue cases were used and the 5-weeks-ahead forecast had the best performance, and the peak of a large outbreak in 2019 was accurately forecasted. The proposed framework in this study suggests the potential of the GEE platform, the LSTM algorithm, as well as historical information for dengue risk forecasting, which can easily be extensively applied to other regions or globally for timely and practical dengue forecasts.

Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City

The present analysis uses the data of confirmed incidence of dengue cases in the metropolitan region of Panama from 1999 to 2017 and climatic variables (air temperature, precipitation, and relative humidity) during the same period to determine if there exists a correlation between these variables. In addition, we compare the predictive performance of two regression models (SARIMA, SARIMAX) and a recurrent neural network model (RNN-LSTM) on the dengue incidence series. For this data from 1999–2014 was used for training and the three subsequent years of incidence 2015–2017 were used for prediction. The results show a correlation coefficient between the climatic variables and the incidence of dengue were low but statistical significant. The RMSE and MAPE obtained for the SARIMAX and RNN-LSTM models were 25.76, 108.44 and 26.16, 59.68, which suggest that any of these models can be used to predict new outbreaks. Although, it can be said that there is a limited role of climatic variables in the outputs the models. The value of this work is that it helps understand the behaviour of cases in a tropical setting as is the Metropolitan Region of Panama City, and provides the basis needed for a much needed early alert system for the region.