The time series seasonal patterns of dengue fever and associated weather variables in Bangkok (2003-2017)

Do Weather Conditions Still Have an Impact on the COVID-19 Pandemic? An Observation of the Mid-2022 COVID-19 Peak in Taiwan

Since the onset of the COVID-19 pandemic in 2019, the role of weather conditions in influencing transmission has been unclear, with results varying across different studies. Given the changes in border policies and the higher vaccination rates compared to earlier conditions, this study aimed to reassess the impact of weather on COVID-19, focusing on local climate effects. We analyzed daily COVID-19 case data and weather factors such as temperature, humidity, wind speed, and a diurnal temperature range from 1 March to 15 August 2022 across six regions in Taiwan. This study found a positive correlation between maximum daily temperature and relative humidity with new COVID-19 cases, whereas wind speed and diurnal temperature range were negatively correlated. Additionally, a significant positive correlation was identified between the unease environmental condition factor (UECF, calculated as RH*Tmax/WS), the kind of Climate Factor Complex (CFC), and confirmed cases. The findings highlight the influence of local weather conditions on COVID-19 transmission, suggesting that such factors can alter environmental comfort and human behavior, thereby affecting disease spread. We also introduced the Fire-Qi Period concept to explain the cyclic climatic variations influencing infectious disease outbreaks globally. This study emphasizes the necessity of considering both local and global climatic effects on infectious diseases.

Temporal trends of dengue cases and deaths from 2007 to 2020 in Belo Horizonte, Brazil

Dengue, a disease with multifactorial determinants, is linked to population susceptibility to circulating viruses and the extent of vector infestation. This study aimed to analyze the temporal trends of dengue cases and deaths in Belo Horizonte, Minas Gerais, Brazil, from 2007 to 2020. Data from the Notifiable Diseases Information System (Sinan) were utilized for the investigation. To assess the disease's progression over the study period and predict its future incidence, time series analyses were conducted using a generalized additive model (GAM) and a seasonal autoregressive integrated moving average (SARIMA) model. Over the study period, a total of 463,566 dengue cases and 125 deaths were reported. Notably, there was an increase in severe cases and deaths, marking hyperendemics characterized by simultaneous virus circulation (79.17% in 2016-50% in 2019). The generalized additive model revealed a non-linear pattern with epidemic peaks in 2010, 2013, 2016, and 2019, indicating an explosive pattern of dengue incidence. The SARIMA (3,1,1) (0,0,0)12 model was validated for each year (2015 to 2019). Comparing the actual and predicted numbers of dengue cases, the model demonstrated its effectiveness for predicting cases in the municipality. The rising number of dengue cases emphasizes the importance of vector surveillance and control. Enhanced models and predictions by local health services will aid in anticipating necessary control measures to combat future epidemics.

Trends of hospitalisation among new admission inpatients with oesophagogastric variceal bleeding in cirrhosis from 2014 to 2019 in the Affiliated Hospital of Southwest Medical University: a single-centre time-series analysis

OBJECTIVES

This study aimed to assess the internal law and time trend of hospitalisation for oesophagogastric variceal bleeding (EGVB) in cirrhosis and develop an effective model to predict the trend of hospitalisation time.

DESIGN

We used a time series covering 72 months to analyse the hospitalisation for EGVB in cirrhosis. The number of inpatients in the first 60 months was used as the training set to establish the autoregressive integrated moving average (ARIMA) model, and the number over the next 12 months was used as the test set to predict and observe their fitting effect.

SETTING AND DATA

Case data of patients with EGVB between January 2014 and December 2019 were collected from the Affiliated Hospital of Southwest Medical University.

OUTCOME MEASURES

The number of monthly hospitalised patients with EGVB in our hospital.

RESULTS

A total of 877 patients were included in the analysis. The proportion of EGVB in patients with cirrhosis was 73% among men and 27% among women. The peak age at hospitalisation was 40-60 years. The incidence of EGVB varied seasonally with two peaks from January to February and October to November, while the lowest number was observed between April and August. Time-series analysis showed that the number of inpatients with EGVB in our hospital increased annually. The sequence after the first-order difference was a stationary series (augmented Dickey-Fuller test p=0.02). ARIMA (0,1,0) (0,1,1)12 with a minimum Akaike Information Criterion value of 260.18 could fit the time trend of EGVB inpatients and had a good short-term prediction effect. The root mean square error and mean absolute error were 2.4347 and 1.9017, respectively.

CONCLUSIONS

The number of hospitalised patients with EGVB at our hospital is increasing annually, with seasonal changes. The ARIMA model has a good prediction effect on the number of hospitalised patients with EGVB in cirrhosis.

Forecasting deep learning-based risk assessment of vector-borne diseases using hybrid methodology

BACKGROUND

Dengue fever is rapidly becoming Malaysia's most pressing health concern, as the reported cases have nearly doubled over the past decade. Without efficacious antiviral medications, vector control remains the primary strategy for battling dengue, while the recently introduced tetravalent immunization is being evaluated. The most significant and dangerous risk increasing recently is vector-borne illnesses. These illnesses induce significant human sickness and are transmitted by blood-feeding arthropods such as fleas, parasites, and mosquitos. A thorough grasp of various factors is necessary to improve prediction accuracy and typically generate inaccurate and unstable predictions, as well as machine learning (ML) models, weather-driven mechanisms, and numerical time series.

OBJECTIVE

In this research, we propose a novel method for forecasting vector-borne disease risk using Radial Basis Function Networks (RBFNs) and the Darts Game Optimizer (DGO) algorithm.

METHODS

The proposed approach entails training the RBFNs with historical disease data and enhancing their parameters with the DGO algorithm. To prepare the RBFNs, we used a massive dataset of vector-borne disease incidences, climate variables, and geographical data. The DGO algorithm proficiently searches the RBFN parameter space, fine-tuning the model's architecture to increase forecast accuracy.

RESULTS

RBFN-DGO provides a potential method for predicting vector-borne disease risk. This study advances predictive demonstrating in public health by shedding light on effectively controlling vector-borne diseases to protect human populations. We conducted extensive testing to evaluate the performance of the proposed method to standard optimization methods and alternative forecasting methods.

CONCLUSION

According to the findings, the RBFN-DGO model beats others in terms of accuracy and robustness in predicting the likelihood of vector-borne illness occurrences.

Modeling Global Monkeypox Infection Spread Data: A Comparative Study of Time Series Regression and Machine Learning Models

The global impact of COVID-19 has heightened concerns about emerging viral infections, among which monkeypox (MPOX) has become a significant public health threat. To address this, our study employs a comprehensive approach using three statistical techniques: Distribution fitting, ARIMA modeling, and Random Forest machine learning to analyze and predict the spread of MPOX in the top ten countries with high infection rates. We aim to provide a detailed understanding of the disease dynamics and model theoretical distributions using country-specific datasets to accurately assess and forecast the disease's transmission. The data from the considered countries are fitted into ARIMA models to determine the best time series regression model. Additionally, we employ the random forest machine learning approach to predict the future behavior of the disease. Evaluating the Root Mean Square Errors (RMSE) for both models, we find that the random forest outperforms ARIMA in six countries, while ARIMA performs better in the remaining four countries. Based on these findings, robust policy-making should consider the best fitted model for each country to effectively manage and respond to the ongoing public health threat posed by monkeypox. The integration of multiple modeling techniques enhances our understanding of the disease dynamics and aids in devising more informed strategies for containment and control.

Socio-economic factors and its influence on the association between temperature and dengue incidence in 61 Provinces of the Philippines, 2010-2019

BACKGROUND

Temperature has a significant impact on dengue incidence, however, changes on the temperature-dengue relationship across axes of socio-economic vulnerability is not well described. This study sought to determine the association between dengue and temperature in multiple locations in the Philippines and explore the effect modification by socio-economic factors.

METHOD

Nationwide dengue cases per province from 2010 to 2019 and data on temperature were obtained from the Philippines' Department of Health-Epidemiological Bureau and ERA5-land, respectively. A generalized additive mixed model (GAMM) with a distributed lag non-linear model was utilized to examine the association between temperature and dengue incidence. We further implemented an interaction analysis in determining how socio-economic factors modify the association. All analyses were implemented using R programming.

RESULTS

Nationwide temperature-dengue risk function was noted to depict an inverted U-shaped pattern. Dengue risk increased linearly alongside increasing mean temperature from 15.8 degrees Celsius and peaking at 27.5 degrees Celsius before declining. However, province-specific analyses revealed significant heterogeneity. Socio-economic factors had varying impact on the temperature-dengue association. Provinces with high population density, less people in urban areas with larger household size, high poverty incidence, higher health spending per capita, and in lower latitudes were noted to exhibit statistically higher dengue risk compared to their counterparts at the upper temperature range.

CONCLUSIONS

This observational study found that temperature was associated with dengue incidence, and that this association is more apparent in locations with high population density, less people in urban areas with larger household size, high poverty incidence, higher health spending per capita, and in lower latitudes. Differences with socio-economic conditions is linked with dengue risk. This highlights the need to develop interventions tailor-fit to local conditions.

Associations between Dengue Incidence, Ecological Factors, and Anthropogenic Factors in Singapore

Singapore experiences endemic dengue. Vector control remains the primary means to reduce transmission due to the lack of available therapeutics. Resource limitations mean that vector-control tools need to be optimized, which can be achieved by studying risk factors related to disease transmission. We developed a statistical modelling framework which can account for a high-resolution and high-dimensional set of covariates to delineate spatio-temporal characteristics that are associated with dengue transmission from 2014 to 2020 in Singapore. We applied the proposed framework to two distinct datasets, stratified based on the primary type of housing within each spatial unit. Generalized additive models reveal non-linear exposure responses between a large range of ecological and anthropogenic factors as well as dengue incidence rates. At values below their mean, lesser mean total daily rainfall (Incidence rate ratio (IRR): 3.75, 95% CI: 1.00-14.05, Mean: 4.40 mm), decreased mean windspeed (IRR: 3.65, 95% CI: 1.87-7.10, Mean: 4.53 km/h), and lower building heights (IRR: 2.62, 95% CI: 1.44-4.77, Mean: 6.5 m) displayed positive associations, while higher than average annual NO2 concentrations (IRR: 0.35, 95% CI: 0.18-0.66, Mean: 13.8 ppb) were estimated to be negatively associated with dengue incidence rates. Our study provides an understanding of associations between ecological and anthropogenic characteristics with dengue transmission. These findings help us understand high-risk areas of dengue transmission, and allows for land-use planning and formulation of vector control policies.

Epicasting: An Ensemble Wavelet Neural Network for forecasting epidemics

Infectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The lack of specific drugs and ready-to-use vaccines to prevent most of these epidemics worsens the situation. These force public health officials and policymakers to rely on early warning systems generated by accurate and reliable epidemic forecasters. Accurate forecasts of epidemics can assist stakeholders in tailoring countermeasures, such as vaccination campaigns, staff scheduling, and resource allocation, to the situation at hand, which could translate to reductions in the impact of a disease. Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics. We analyze various epidemic time series datasets using a maximal overlap discrete wavelet transform (MODWT) based autoregressive neural network and call it Ensemble Wavelet Neural Network (EWNet) model. MODWT techniques effectively characterize non-stationary behavior and seasonal dependencies in the epidemic time series and improve the nonlinear forecasting scheme of the autoregressive neural network in the proposed ensemble wavelet network framework. From a nonlinear time series viewpoint, we explore the asymptotic stationarity of the proposed EWNet model to show the asymptotic behavior of the associated Markov Chain. We also theoretically investigate the effect of learning stability and the choice of hidden neurons in the proposal. From a practical perspective, we compare our proposed EWNet framework with twenty-two statistical, machine learning, and deep learning models for fifteen real-world epidemic datasets with three test horizons using four key performance indicators. Experimental results show that the proposed EWNet is highly competitive compared to the state-of-the-art epidemic forecasting methods.

PICTUREE-Aedes: A Web Application for Dengue Data Visualization and Case Prediction

Dengue fever remains a significant public health concern in many tropical and subtropical countries, and there is still a need for a system that can effectively combine global risk assessment with timely incidence forecasting. This research describes an integrated application called PICTUREE-Aedes, which can collect and analyze dengue-related data, display simulation results, and forecast outbreak incidence. PICTUREE-Aedes automatically updates global temperature and precipitation data and contains historical records of dengue incidence (1960-2012) and Aedes mosquito occurrences (1960-2014) in its database. The application utilizes a mosquito population model to estimate mosquito abundance, dengue reproduction number, and dengue risk. To predict future dengue outbreak incidence, PICTUREE-Aedes applies various forecasting techniques, including the ensemble Kalman filter, recurrent neural network, particle filter, and super ensemble forecast, which are all based on user-entered case data. The PICTUREE-Aedes' risk estimation identifies favorable conditions for potential dengue outbreaks, and its forecasting accuracy is validated by available outbreak data from Cambodia.

Prediction of dengue cases using the attention-based long short-term memory (LSTM) approach

This research proposes a 'temporal attention' addition for long-short term memory (LSTM) models for dengue prediction. The number of monthly dengue cases was collected for each of five Malaysian states i.e. Selangor, Kelantan, Johor, Pulau Pinang, and Melaka from 2011 to 2016. Climatic, demographic, geographic and temporal attributes were used as covariates. The proposed LSTM models with temporal attention was compared with several benchmark models including a linear support vector machine (LSVM), a radial basis function support vector machine (RBFSVM), a decision tree (DT), a shallow neural network (SANN) and a deep neural network (D-ANN). In addition, experiments were conducted to analyze the impact of look-back settings on each model performance. The results showed that the attention LSTM (A-LSTM) model performed best, with the stacked, attention LSTM (SA-LSTM) one in second place. The LSTM and stacked LSTM (S-LSTM) models performed almost identically but with the accuracy improved by the attention mechanism was added. Indeed, they were both found to be superior to the benchmark models mentioned above. The best results were obtained when all attributes were included in the model. The four models (LSTM, S-LSTM, A-LSTM and SA-LSTM) were able to accurately predict dengue presence 1-6 months ahead. Our findings provide a more accurate dengue prediction model than previously used, with the prospect of also applying this approach in other geographic areas.

Seasonality influences key physiological components contributing to Culex pipiens vector competence

Mosquitoes are the most important animal vector of disease on the planet, transmitting a variety of pathogens of both medical and veterinary importance. Mosquito-borne diseases display distinct seasonal patterns driven by both environmental and biological variables. However, an important, yet unexplored component of these patterns is the potential for seasonal influences on mosquito physiology that may ultimately influence vector competence. To address this question, we selected Culex pipiens, a primary vector of the West Nile virus (WNV) in the temperate United States, to examine the seasonal impacts on mosquito physiology by examining known immune and bacterial components implicated in mosquito arbovirus infection. Semi-field experiments were performed under spring, summer, and late-summer conditions, corresponding to historically low-, medium-, and high-intensity periods of WNV transmission, respectively. Through these experiments, we observed differences in the expression of immune genes and RNA interference (RNAi) pathway components, as well as changes in the distribution and abundance of Wolbachia in the mosquitoes across seasonal cohorts. Together, these findings support the conclusion that seasonal changes significantly influence mosquito physiology and components of the mosquito microbiome, suggesting that seasonality may impact mosquito susceptibility to pathogen infection, which could account for the temporal patterns in mosquito-borne disease transmission.

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.

Effects of high temperatures and heatwaves on dengue fever: a systematic review and meta-analysis

BACKGROUND

Studies have shown that dengue virus transmission increases in association with ambient temperature. We performed a systematic review and meta-analysis to assess the effect of both high temperatures and heatwave events on dengue transmission in different climate zones globally.

METHODS

A systematic literature search was conducted in PubMed, Scopus, Embase, and Web of Science from January 1990 to September 20, 2022. We included peer reviewed original observational studies using ecological time series, case crossover, or case series study designs reporting the association of high temperatures and heatwave with dengue and comparing risks over different exposures or time periods. Studies classified as case reports, clinical trials, non-human studies, conference abstracts, editorials, reviews, books, posters, commentaries; and studies that examined only seasonal effects were excluded. Effect estimates were extracted from published literature. A random effects meta-analysis was performed to pool the relative risks (RRs) of dengue infection per 1 °C increase in temperature, and further subgroup analyses were also conducted. The quality and strength of evidence were evaluated following the Navigation Guide systematic review methodology framework. The review protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO).

FINDINGS

The study selection process yielded 6367 studies. A total of 106 studies covering more than four million dengue cases fulfilled the inclusion criteria; of these, 54 studies were eligible for meta-analysis. The overall pooled estimate showed a 13% increase in risk of dengue infection (RR = 1.13; 95% confidence interval (CI): 1.11-1.16, I2 = 98.0%) for each 1 °C increase in high temperatures. Subgroup analyses by climate zones suggested greater effects of temperature in tropical monsoon climate zone (RR = 1.29, 95% CI: 1.11-1.51) and humid subtropical climate zone (RR = 1.20, 95% CI: 1.15-1.25). Heatwave events showed association with an increased risk of dengue infection (RR = 1.08; 95% CI: 0.95-1.23, I2 = 88.9%), despite a wide confidence interval. The overall strength of evidence was found to be "sufficient" for high temperatures but "limited" for heatwaves. Our results showed that high temperatures increased the risk of dengue infection, albeit with varying risks across climate zones and different levels of national income.

INTERPRETATION

High temperatures increased the relative risk of dengue infection. Future studies on the association between temperature and dengue infection should consider local and regional climate, socio-demographic and environmental characteristics to explore vulnerability at local and regional levels for tailored prevention.

FUNDING

Australian Research Council Discovery Program.

The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study

BACKGROUND

Traditionally, dengue prevention and control rely on vector control programs and reporting of symptomatic cases to a central health agency. However, case reporting is often delayed, and the true burden of dengue disease is often underestimated. Moreover, some countries do not have routine control measures for vector control. Therefore, researchers are constantly assessing novel data sources to improve traditional surveillance systems. These studies are mostly carried out in big territories and rarely in smaller endemic regions, such as Martinique and the Lesser Antilles.

OBJECTIVE

The aim of this study was to determine whether heterogeneous real-world data sources could help reduce reporting delays and improve dengue monitoring in Martinique island, a small endemic region.

METHODS

Heterogenous data sources (hospitalization data, entomological data, and Google Trends) and dengue surveillance reports for the last 14 years (January 2007 to February 2021) were analyzed to identify associations with dengue outbreaks and their time lags.

RESULTS

The dengue hospitalization rate was the variable most strongly correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.70) with a time lag of -3 weeks. Weekly entomological interventions were also correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.59) with a time lag of -2 weeks. The most correlated query from Google Trends was the "Dengue" topic restricted to the Martinique region (Pearson correlation coefficient=0.637) with a time lag of -3 weeks.

CONCLUSIONS

Real-word data are valuable data sources for dengue surveillance in smaller territories. Many of these sources precede the increase in dengue cases by several weeks, and therefore can help to improve the ability of traditional surveillance systems to provide an early response in dengue outbreaks. All these sources should be better integrated to improve the early response to dengue outbreaks and vector-borne diseases in smaller endemic territories.

Modeling and Forecasting Monkeypox Cases Using Stochastic Models

BACKGROUND

Monkeypox virus is gaining attention due to its severity and spread among people. This study sheds light on the modeling and forecasting of new monkeypox cases. Knowledge about the future situation of the virus using a more accurate time series and stochastic models is required for future actions and plans to cope with the challenge.

METHODS

We conduct a side-by-side comparison of the machine learning approach with the traditional time series model. The multilayer perceptron model (MLP), a machine learning technique, and the Box-Jenkins methodology, also known as the ARIMA model, are used for classical modeling. Both methods are applied to the Monkeypox cumulative data set and compared using different model selection criteria such as root mean square error, mean square error, mean absolute error, and mean absolute percentage error.

RESULTS

With a root mean square error of 150.78, the monkeypox series follows the ARIMA (7,1,7) model among the other potential models. Comparatively, we use the multilayer perceptron (MLP) model, which employs the sigmoid activation function and has a different number of hidden neurons in a single hidden layer. The root mean square error of the MLP model, which uses a single input and ten hidden neurons, is 54.40, significantly lower than that of the ARIMA model. The actual confirmed cases versus estimated or fitted plots also demonstrate that the multilayer perceptron model has a better fit for the monkeypox data than the ARIMA model.

CONCLUSIONS AND RECOMMENDATION

When it comes to predicting monkeypox, the machine learning method outperforms the traditional time series. A better match can be achieved in future studies by applying the extreme learning machine model (ELM), support vector machine (SVM), and some other methods with various activation functions. It is thus concluded that the selected data provide a real picture of the virus. If the situations remain the same, governments and other stockholders should ensure the follow-up of Standard Operating Procedures (SOPs) among the masses, as the trends will continue rising in the upcoming 10 days. However, governments should take some serious interventions to cope with the virus.

LIMITATION

In the ARIMA models selected for forecasting, we did not incorporate the effect of covariates such as the effect of net migration of monkeypox virus patients, government interventions, etc.

A cross-tabulated analysis for the influence of climate conditions on the incidence of dengue fever in Jeddah City, Saudi Arabia during 2006-2009

OBJECTIVE

Increased temperature and humidity across the world and emergence of mosquito-borne diseases, notably dengue both continue to present public health problems, but their relationship is not clear as conflicting evidence abound on the association between climate conditions and risk of dengue fever. This characterization is important as mitigation of climate change-related variables will contribute toward efficient planning of health services. The purpose of this study was to determine whether humidity in addition to high temperatures increase the risk of dengue transmission.

METHODS

We have assessed the joint association between temperature and humidity with the incidence of dengue fever at Jeddah City in Saudi Arabia. We obtained weekly data from Jeddah City on temperature and humidity between 2006 and 2009 for 200 weeks starting week 1/2006 and ending week 53/2009. We also collected incident case data on dengue fever in Jeddah City.

RESULTS

The cross-tabulated analysis showed an association between temperature or humidity conditions and incident cases of dengue. Our data found that hot and dry conditions were associated with a high risk of dengue incidence in Jeddah City.

CONCLUSION

Hot and dry conditions are risk factors for dengue fever.

The association between dengue case and climate: A systematic review and meta-analysis

Although previous research frequently indicates that climate factors impact dengue transmission, the results are inconsistent. Therefore, this systematic review and meta-analysis highlights and address the complex global health problems towards the human-environment interface and the inter-relationship between these variables. For this purpose, four online electronic databases were searched to conduct a systematic assessment of published studies reporting the association between dengue cases and climate between 2010 and 2022. The meta-analysis was conducted using random effects to assess correlation, publication bias and heterogeneity. The final assessment included eight studies for both systematic review and meta-analysis. A total of four meta-analyses were conducted to evaluate the correlation of dengue cases with climate variables, namely precipitation, temperature, minimum temperature and relative humidity. The highest correlation is observed for precipitation between 83 mm and 15 mm (r = 0.38, 95% CI = 0.31, 0.45), relative humidity between 60.5% and 88.7% (r = 0.30, 95% CI = 0.23, 0.37), minimum temperature between 6.5 °C and 21.4 °C (r = 0.28, 95% CI = 0.05, 0.48) and mean temperature between 21.0 °C and 29.8 °C (r = 0.07, 95% CI = -0.1, 0.24). Thus, the influence of climate variables on the magnitude of dengue cases in terms of their distribution, frequency, and prevailing variables was established and conceptualised. The results of this meta-analysis enable multidisciplinary collaboration to improve dengue surveillance, epidemiology, and prevention programmes.

Spatio-temporal dynamics of three diseases caused by Aedes-borne arboviruses in Mexico

Background

The intensity of transmission of Aedes-borne viruses is heterogeneous, and multiple factors can contribute to variation at small spatial scales. Illuminating drivers of heterogeneity in prevalence over time and space would provide information for public health authorities. The objective of this study is to detect the spatiotemporal clusters and determine the risk factors of three major Aedes-borne diseases, Chikungunya virus (CHIKV), Dengue virus (DENV), and Zika virus (ZIKV) clusters in Mexico.

Methods

We present an integrated analysis of Aedes-borne diseases (ABDs), the local climate, and the socio-demographic profiles of 2469 municipalities in Mexico. We used SaTScan to detect spatial clusters and utilize the Pearson correlation coefficient, Randomized Dependence Coefficient, and SHapley Additive exPlanations to analyze the influence of socio-demographic and climatic factors on the prevalence of ABDs. We also compare six machine learning techniques, including XGBoost, decision tree, Support Vector Machine with Radial Basis Function kernel, K nearest neighbors, random forest, and neural network to predict risk factors of ABDs clusters.

Results

DENV is the most prevalent of the three diseases throughout Mexico, with nearly 60.6% of the municipalities reported having DENV cases. For some spatiotemporal clusters, the influence of socio-economic attributes is larger than the influence of climate attributes for predicting the prevalence of ABDs. XGBoost performs the best in terms of precision-measure for ABDs prevalence.

Conclusions

Both socio-demographic and climatic factors influence ABDs transmission in different regions of Mexico. Future studies should build predictive models supporting early warning systems to anticipate the time and location of ABDs outbreaks and determine the stand-alone influence of individual risk factors and establish causal mechanisms.

Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil

Efficient and accurate dengue risk prediction is an important basis for dengue prevention and control, which faces challenges, such as downloading and processing multi-source data to generate risk predictors and consuming significant time and computational resources to train and validate models locally. In this context, this study proposed a framework for dengue risk prediction by integrating big geospatial data cloud computing based on Google Earth Engine (GEE) platform and artificial intelligence modeling on the Google Colab platform. It enables defining the epidemiological calendar, delineating the predominant area of dengue transmission in cities, generating the data of risk predictors, and defining multi-date ahead prediction scenarios. We implemented the experiments based on weekly dengue cases during 2013-2020 in the Federal District and Fortaleza, Brazil to evaluate the performance of the proposed framework. Four predictors were considered, including total rainfall (R_sum), mean temperature (T_mean), mean relative humidity (RH_mean), and mean normalized difference vegetation index (NDVI_mean). Three models (i.e., random forest (RF), long-short term memory (LSTM), and LSTM with attention mechanism (LSTM-ATT)), and two modeling scenarios (i.e., modeling with or without dengue cases) were set to implement 1- to 4-week ahead predictions. A total of 24 models were built, and the results showed in general that LSTM and LSTM-ATT models outperformed RF models; modeling could benefit from using historical dengue cases as one of the predictors, and it makes the predicted curve fluctuation more stable compared with that only using climate and environmental factors; attention mechanism could further improve the performance of LSTM models. This study provides implications for future dengue risk prediction in terms of the effectiveness of GEE-based big geospatial data processing for risk predictor generation and Google Colab-based risk modeling and presents the benefits of using historical dengue data as one of the input features and the attention mechanism for LSTM modeling.

Forecasting the incidence of dengue in Bangladesh-Application of time series model

Background

Dengue is an alarming public health concern in terms of its preventive and curative measures among people in Bangladesh; moreover, its sudden outbreak created a lot of suffering among people in 2018. Considering the greater burden of disease in larger epidemic years and the difficulty in understanding current and future needs, it is highly needed to address early warning systems to control epidemics from the earliest.

Objective

The study objective was to select the most appropriate model for dengue incidence and using the selected model, the authors forecast the future dengue outbreak in Bangladesh.

Methods and Materials

This study considered a secondary data set of monthly dengue occurrences over the period of January 2008 to January 2020. Initially, the authors found the suitable model from Autoregressive Integrated Moving Average (ARIMA), Error, Trend, Seasonal (ETS) and Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS) models with the help of selected model selection criteria and finally employing the selected model make forecasting of dengue incidences in Bangladesh.

Results

Among ARIMA, ETS, and TBATS models, the ARIMA model performs better than others. The Box-Jenkin's procedure is applicable here and it is found that the best-selected model to forecast the dengue outbreak in the context of Bangladesh is ARIMA (2,1,2).

Conclusion

Before establishing a comprehensive plan for future combating strategies, it is vital to understand the future scenario of dengue occurrence. With this in mind, the authors aimed to select an appropriate model that might predict dengue fever outbreaks in Bangladesh. The findings revealed that dengue fever is expected to become more frequent in the future. The authors believe that the study findings will be helpful to take early initiatives to combat future dengue outbreaks.

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.

The Effectiveness of Net to Reduce the Entomological Indices in Dengue-Endemic Areas in Balikpapan, Indonesia

BACKGROUND: Drums and cisterns are ubiquitous water storages in Indonesian households, seldom being drained and left open to create access for the rainwater, providing a favorable breeding site for dengue vector. The bigger the container, the more it produces immature mosquitoes that are soon to be mature, increasing the entomological indices and raising the potency of cases in the area. Previous studies revealed that the net covering the reservoir was able to effectively protect the water from mosquito oviposition; therefore, a modification of the net was made. AIM: The aim of this study is to discover whether the net as a cover for water containers is effective in reducing the entomological indices in dengue-endemic areas. METHODS: The quasi-experimental study with pretest and posttest control group design, involved 3 intervention and 3 control clusters, 150 houses which have 672 water-holding containers with 116 large containers were intervened with non-insecticide tulle nets for 3 months. The larval presence data were performed by larval survey. RESULTS: It revealed that net reduced the container index (CI) in intervened large containers 18%–84% as well as the environment entomological indices in general in study areas: CI decreased 75%–79%, house index decreased 65%–70%, and Breteau index decreased 75.5%–78.7%, while Free Larva Index rose 73.7%–88%. CONCLUSIONS: The nets had lowered the CI in the intervened large container and affected the entomological indices of the surrounding environment, by blocking the mosquitos-water contact and preventing the young mosquitos that had developed in the containers from flying out.

Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries

The COVID-19 pandemic is a serious threat to all of us. It has caused an unprecedented shock to the world’s economy, and it has interrupted the lives and livelihood of millions of people. In the last two years, a large body of literature has attempted to forecast the main dimensions of the COVID-19 outbreak using a wide set of models. In this paper, I forecast the short- to mid-term cumulative deaths from COVID-19 in 12 hard-hit big countries around the world as of 20 August 2021. The data used in the analysis were extracted from the Our World in Data COVID-19 dataset. Both non-seasonal and seasonal autoregressive integrated moving averages (ARIMA and SARIMA) were estimated. The analysis showed that: (i) ARIMA/SARIMA forecasts were sufficiently accurate in both the training and test set by always outperforming the simple alternative forecasting techniques chosen as benchmarks (Mean, Naïve, and Seasonal Naïve); (ii) SARIMA models outperformed ARIMA models in 47 out 48 metrics (in forecasting future values), i.e., on 97.9% of all the considered forecast accuracy measures (mean absolute error [MAE], mean absolute percentage error [MAPE], mean absolute scaled error [MASE], and the root mean squared error [RMSE]), suggesting a clear seasonal pattern in the data; and (iii) the forecasted values from SARIMA models fitted very well the observed (real-time) data for the period 21 August 2021–19 September 2021 for almost all the countries analyzed. This article shows that SARIMA can be safely used for both the short- and medium-term predictions of COVID-19 deaths. Thus, this approach can help government authorities to monitor and manage the huge pressure that COVID-19 is exerting on national healthcare systems.

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.

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.

Prediction of dengue annual incidence using seasonal climate variability in Bangladesh between 2000 and 2018

The incidence of dengue has increased rapidly in Bangladesh since 2010 with an outbreak in 2018 reaching a historically high number of cases, 10,148. A better understanding of the effects of climate variability before dengue season on the increasing incidence of dengue in Bangladesh can enable early warning of future outbreaks. We developed a generalized linear model to predict the number of annual dengue cases based on monthly minimum temperature, rainfall and sunshine prior to dengue season. Variable selection and leave-one-out cross-validation were performed to identify the best prediction model and to evaluate the model's performance. Our model successfully predicted the largest outbreak in 2018, with 10,077 cases (95% CI: [9,912-10,276]), in addition to smaller outbreaks in five different years (2003, 2006, 2010, 2012 and 2014) and successfully identified the increasing trend in cases between 2010 and 2018. We found that temperature was positively associated with the annual incidence during the late winter months (between January and March) but negatively associated during the early summer (between April and June). Our results might be suggest an optimal minimum temperature for mosquito growth of 21-23°C. This study has implications for understanding how climate variability has affected recent dengue expansion in neighbours of Bangladesh (such as northern India and Southeast Asia).

Epidemiological screening and serotyping analysis of dengue fever in the Southwestern region of Saudi Arabia

Dengue is an acute systemic viral disease that has been developed globally in both chronic and epidemic transmission periods. Dengue virus (DENV) is a member of the Flavivirus genus of the Flaviviridae family, which endangers public health. Limited studies have been performed in the Saudi Arabia and there are no epidemiological as well as molecular screening of DENV in the Southwestern region and this current study was conducted on the epidemiology of dengue in the Southwestern region of Saudi Arabia. Simultaneously, we have screened the 100 patients for DENV using the real-time reverse transcriptase polymerase chain reaction (RT-PCR) assay. The current study results confirmed that 6637 people from various hospitals and locations in Jazan, Southwestern regions, were enrolled in this study from 2012 to 2020. The overall mean age was 30.02 ± 18.01 years, with 62.8% of males and 37.2% of females enrolled. This study included nearly three-fourths of the Saudi participants and one-fourth of the expatriates, and 56.6% of the positive cases were enrolled. In 2019, the most instances were enrolled, with 44% of positive cases. When screened using the RT-PCR assay, 93% of the positive patients were recruited, according to the quality control analysis. In conclusion, the current study results confirmed the prevalence of DENV was increased drastically since 2012 to 2020. High number of cases were registered prior to the Pandemic. The screening for DENV was performed with RT-PCR assay and NSI antigen should also be implemented to cross-check the results which was previously performed with RT-PCR analysis.

Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review

Background

Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes.

Methodology/Principal findings

We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descriptive studies were not included. Among the 119 studies included, 67% were published between 2016 and 2020, and 39% used at least one novel data stream. The aim of the included studies was to predict a dengue-related outcome (55%), assess the validity of data sources for dengue surveillance (23%), or both (22%). Most studies (60%) used a machine learning approach. Studies on dengue prediction compared different prediction models, or identified significant predictors among several covariates in a model. The most significant predictors were rainfall (43%), temperature (41%), and humidity (25%). The two models with the highest performances were Neural Networks and Decision Trees (52%), followed by Support Vector Machine (17%). We cannot rule out a selection bias in our study because of our two main limitations: we did not include preprints and could not obtain the opinion of other international experts.

Conclusions/Significance

Combining real-world data and Big Data with machine learning methods is a promising approach to improve dengue prediction and monitoring. Future studies should focus on how to better integrate all available data sources and methods to improve the response and dengue management by stakeholders.

Dengue is one of the most important arbovirus infections in the world and its public health, societal and economic burden is increasing. Although the majority of dengue cases are asymptomatic or mild, severe disease forms can lead to death. For this reason, early diagnosis and monitoring of dengue are crucial to decrease mortality. However, most endemic regions still rely on traditional monitoring methods, despite the growing availability of novel data sources and data-driven methods based on real-world data, Big Data, and machine learning algorithms. In this systematic review, we identified and analyzed studies that used these novel approaches for dengue monitoring and/or prediction. We found that novel data streams, such as Internet search engines and social media platforms, and machine learning methods can be successfully used to improve dengue management, but are still vastly ignored in real life. These approaches should be combined with traditional methods to help stakeholders better prepare for each outbreak and improve early responsiveness.

Forecasting waved daily COVID-19 death count series with a novel combination of segmented Poisson model and ARIMA models

Autoregressive Integrated Moving Average (ARIMA) models have been widely used to forecast and model the development of various infectious diseases including COVID-19 outbreaks; however, such use of ARIMA models does not respect the count nature of the pandemic development data. For example, the daily COVID-19 death count series data for Canada and the United States (USA) are generally skewed with lots of low counts. In addition, there are generally waved patterns with turning points influenced by government major interventions against the spread of COVID-19 during different periods and seasons. In this study, we propose a novel combination of the segmented Poisson model and ARIMA models to handle these features and correlation structures in a two-stage process. The first stage of this process is a generalization of trend analysis of time series data. Our approach is illustrated with forecasting and modeling of daily COVID-19 death count series data for Canada and the USA.

Dengue Transmission Mapping with Weather-Based Predictive Model in Three Southernmost Provinces of Thailand

This study aimed to show maps and analyses that display dengue cases and weather-related factors on dengue transmission in the three southernmost provinces of Thailand, namely Pattani, Yala, and Narathiwat provinces. Data on the number of dengue cases and weather variables including rainfall, rainy day, mean temperature, min temperature, max temperature, relative humidity, and air pressure for the period from January 2015 to December 2019 were obtained from the Bureau of Epidemiology, Ministry of Public Health and the Meteorological Department of Southern Thailand, respectively. Spearman rank correlation test was performed at lags from zero to two months and the predictive modeling used time series Poisson regression analysis. The distribution of dengue cases showed that in Pattani and Yala provinces the most dengue cases occurred in June. Narathiwat province had the most dengue cases occurring in August. The air pressure, relative humidity, rainfall, rainy day, and min temperature are the main predictors in Pattani province, while air pressure, rainy day, and max/mean temperature seem to play important roles in the number of dengue cases in Yala and Narathiwat provinces. The goodness-of-fit analyses reveal that the model fits the data reasonably well. The results provide scientific information for creating effective dengue control programs in the community, and the predictive model can support decision making in public health organizations and for management of the environmental risk area.

Ecological, Social, and Other Environmental Determinants of Dengue Vector Abundance in Urban and Rural Areas of Northeastern Thailand

Aedes aegypti is the main vector of dengue globally. The variables that influence the abundance of dengue vectors are numerous and complex. This has generated a need to focus on areas at risk of disease transmission, the spatial-temporal distribution of vectors, and the factors that modulate vector abundance. To help guide and improve vector-control efforts, this study identified the ecological, social, and other environmental risk factors that affect the abundance of adult female and immature Ae. aegypti in households in urban and rural areas of northeastern Thailand. A one-year entomological study was conducted in four villages of northeastern Thailand between January and December 2019. Socio-demographic; self-reported prior dengue infections; housing conditions; durable asset ownership; water management; characteristics of water containers; knowledge, attitudes, and practices (KAP) regarding climate change and dengue; and climate data were collected. Household crowding index (HCI), premise condition index (PCI), socio-economic status (SES), and entomological indices (HI, CI, BI, and PI) were calculated. Negative binomial generalized linear models (GLMs) were fitted to identify the risk factors associated with the abundance of adult females and immature Ae. aegypti. Urban sites had higher entomological indices and numbers of adult Ae. aegypti mosquitoes than rural sites. Overall, participants’ KAP about climate change and dengue were low in both settings. The fitted GLM showed that a higher abundance of adult female Ae. aegypti was significantly (p < 0.05) associated with many factors, such as a low education level of household respondents, crowded households, poor premise conditions, surrounding house density, bathrooms located indoors, unscreened windows, high numbers of wet containers, a lack of adult control, prior dengue infections, poor climate change adaptation, dengue, and vector-related practices. Many of the above were also significantly associated with a high abundance of immature mosquito stages. The GLM model also showed that maximum and mean temperature with four-and one-to-two weeks of lag were significant predictors (p < 0.05) of the abundance of adult and immature mosquitoes, respectively, in northeastern Thailand. The low KAP regarding climate change and dengue highlights the engagement needs for vector-borne disease prevention in this region. The identified risk factors are important for the critical first step toward developing routine Aedes surveillance and reliable early warning systems for effective dengue and other mosquito-borne disease prevention and control strategies at the household and community levels in this region and similar settings elsewhere.

Clinical and epidemiologic characteristics associated with dengue fever in 2011-2016 in Bang Phae district, Ratchaburi province, Thailand

BACKGROUND

Dengue is a major public health problem in Thailand, but data are often focused on certain dengue-endemic areas. Methods: To better understand dengue epidemiology and clinical characteristics in Thailand, a fever surveillance study was conducted among patients aged 1-55 years, who presented with non-localized febrile illness at Bang Phae Community Hospital in Ratchaburi province, Thailand from October 2011 to September 2016.

RESULTS

Among 951 febrile episodes, 130 were dengue-confirmed. Individuals aged 10-14 years were mostly affected, followed by those 15-19 years-of-age, with about 15% of dengue-confirmed cases from adults 25 years and older. There were annual peaks of dengue occurrence between June-November. Most prevalent serotype in circulation was DENV-2 in 2012, DENV-3 in 2014, and DENV-4 & -3 in 2015. Among dengue cases, 65% were accurately detected using the dengue NS1 RDT. Detection rate was similar between secondary and primary dengue cases where 66% of secondary vs. 60% of primary dengue cases had positive results on the NS1 RDT. Among dengue cases, 66% were clinically diagnosed with suspected dengue or DHF, prior to lab confirmation. Dengue was positively associated with rash, headache, hematemesis and alterations to consciousness, when compared to non-dengue. Dengue patients were 10.6 times more likely to be hospitalized, compared to non-dengue cases. Among dengue cases, 95 were secondary and 35 were primary infections. There were 8 suspected DHF cases and all were identified to be secondary dengue. Secondary dengue cases were 3.5 times more likely to be hospitalized compared to primary dengue cases. Although the majority of our dengue-positive patients were secondary dengue cases, with few patients showing manifestations of DHF, our dengue cases were mostly mild disease. Even among children < 10 years-of-age, 61% had secondary infection and the rate of secondary infection increased with age.

CONCLUSION

While the majority of dengue-confirmed cases were children, almost three-quarters of dengue-confirmed cases in this study were secondary dengue. Our study results consistent with previous data from the country confirm the hyperendemic transmission of DENV in Thailand, even in the non-epidemic years. With various interventions becoming available for dengue prevention and control, including dengue vaccines, decision-making on future implementation strategies should be based on such burden of disease data.

Spatial and temporal analysis of hospitalized dengue patients in Bandung: demographics and risk

Background

Bandung, the fourth largest city in Indonesia and capital of West Java province, has been considered a major endemic area of dengue, and studies show that the incidence in this city could increase and spread rapidly. At the same time, estimation of incidence could be inaccurate due to a lack of reliable surveillance systems. To provide strategic information for the dengue control program in the face of limited capacity, this study used spatial pattern analysis of a possible outbreak of dengue cases, through the Geographic Information System (GIS). To further enhance the information needed for effective policymaking, we also analyzed the demographic pattern of dengue cases.

Methods

Monthly reports of dengue cases from January 2014 to December 2016 from 16 hospitals in Bandung were collected as the database, which consisted of address, sex, age, and code to anonymize the patients. The address was then transformed into geocoding and used to estimate the relative risk of a particular area’s developing a cluster of dengue cases. We used the kernel density estimation method to analyze the dynamics of change of dengue cases.

Results

The model showed that the spatial cluster of the relative risk of dengue incidence was relatively unchanged for 3 years. Dengue high-risk areas predominated in the southern and southeastern parts of Bandung, while low-risk areas were found mostly in its western and northeastern regions. The kernel density estimation showed strong cluster groups of dengue cases in the city.

Conclusions

This study demonstrated a strong pattern of reported cases related to specific demographic groups (males and children). Furthermore, spatial analysis using GIS also visualized the dynamic development of the aggregation of disease incidence (hotspots) for dengue cases in Bandung. These data may provide strategic information for the planning and design of dengue control programs.

Geographical heterogeneity and socio-ecological risk profiles of dengue in Jakarta, Indonesia

The aim of this study was to assess the role of climate variability on the incidence of dengue fever (DF), an endemic arboviral infection existing in Jakarta, Indonesia. The work carried out included analysis of the spatial distribution of confirmed DF cases from January 2007 to December 2018 characterising the sociodemographical and ecological factors in DF high-risk areas. Spearman's rank correlation was used to examine the relationship between DF incidence and climatic factors. Spatial clustering and hotspots of DF were examined using global Moran's I statistic and the local indicator for spatial association analysis. Classification and regression tree (CART) analysis was performed to compare and identify demographical and socio-ecological characteristics of the identified hotspots and low-risk clusters. The seasonality of DF incidence was correlated with precipitation (r=0.254, P<0.01), humidity (r=0.340, P<0.01), dipole mode index (r= -0.459, P<0.01) and Tmin (r= -0.181, P<0.05). DF incidence was spatially clustered at the village level (I=0.294, P<0.001) and 22 hotspots were identified with a concentration in the central and eastern parts of Jakarta. CART analysis showed that age and occupation were the most important factors explaining DF clustering. Areaspecific and population-targeted interventions are needed to improve the situation among those living in the identified DF high-risk areas in Jakarta.

Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections

The outbreak of novel coronavirus (COVID-19) attracted worldwide attention. It has posed a significant challenge for the global economies, especially the healthcare sector. Even with a robust healthcare system, countries were not prepared for the ramifications of COVID-19. Several statistical, dynamic, and mathematical models of the COVID-19 outbreak including the SEIR model have been developed to analyze the infection its transmission dynamics. The objective of this research is to use public data to study the properties associated with the COVID-19 pandemic to develop a dynamic hybrid model based on SEIRD and ascertainment rate with automatically selected parameters. The proposed model consists of two parts: the modified SEIRD dynamic model and ARIMA models. We fit SEIRD model parameters against historical values of infected, recovered and deceased population divided by ascertainment rate, which, in turn, is also a parameter of the model. Residuals of the first model for infected, recovered, and deceased populations are then corrected using ARIMA models. The model can analyze the input data in real-time and provide long- and short-term forecasts with confidence intervals. The model was tested and validated on the US COVID statistics dataset from the COVID Tracking Project. For validation, we use unseen recent statistical data. We use five common measures to estimate model prediction ability: MAE, MSE, MLSE, Normalized MAE, and Normalized MSE. We proved a great model ability to make accurate predictions of infected, recovered, and deceased patients. The output of the model can be used by the government, private sectors, and policymakers to reduce health and economic risks significantly improved consumer credit scoring.

COVID-19 prevalence estimation: Four most affected African countries

The world at large has been confronted with several disease outbreak which has posed and still posing a serious menace to public health globally. Recently, COVID-19 a new kind of coronavirus emerge from Wuhan city in China and was declared a pandemic by the World Health Organization. There has been a reported case of about 8622985 with global death of 457,355 as of 15.05 GMT, June 19, 2020. South-Africa, Egypt, Nigeria and Ghana are the most affected African countries with this outbreak. Thus, there is a need to monitor and predict COVID-19 prevalence in this region for effective control and management. Different statistical tools and time series model such as the linear regression model and autoregressive integrated moving average (ARIMA) models have been applied for disease prevalence/incidence prediction in different diseases outbreak. However, in this study, we adopted the ARIMA model to forecast the trend of COVID-19 prevalence in the aforementioned African countries. The datasets examined in this analysis spanned from February 21, 2020, to June 16, 2020, and was extracted from the World Health Organization website. ARIMA models with minimum Akaike information criterion correction (AICc) and statistically significant parameters were selected as the best models. Accordingly, the ARIMA (0,2,3), ARIMA (0,1,1), ARIMA (3,1,0) and ARIMA (0,1,2) models were chosen as the best models for SA, Nigeria, and Ghana and Egypt, respectively. Forecasting was made based on the best models. It is noteworthy to claim that the ARIMA models are appropriate for predicting the prevalence of COVID-19. We noticed a form of exponential growth in the trend of this virus in Africa in the days to come. Thus, the government and health authorities should pay attention to the pattern of COVID-19 in Africa. Necessary plans and precautions should be put in place to curb this pandemic in Africa.

Modeling and Forecasting of COVID-19 Growth Curve in India

In this article, we analyze the growth pattern of COVID-19 pandemic in India from March 4 to July 11 using regression analysis (exponential and polynomial), auto-regressive integrated moving averages (ARIMA) model as well as exponential smoothing and Holt–Winters models. We found that the growth of COVID-19 cases follows a power regime of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({t}^{2}, t,...)$$\end{document}(t2,t,...) after the exponential growth. We found the optimal change points from where the COVID-19 cases shifted their course of growth from exponential to quadratic and then from quadratic to linear. After that, we saw a sudden spike in the course of the spread of COVID-19 and the growth moved from linear to quadratic and then to quartic, which is alarming. We have also found the best fitted regression models using the various criteria, such as significant p-values, coefficients of determination and ANOVA, etc. Further, we search the best-fitting ARIMA model for the data using the AIC (Akaike Information Criterion) and provide the forecast of COVID-19 cases for future days. We also use usual exponential smoothing and Holt–Winters models for forecasting purpose. We further found that the ARIMA (5, 2, 5) model is the best-fitting model for COVID-19 cases in India.

Estimation of COVID-19 prevalence in Italy, Spain, and France

At the end of December 2019, coronavirus disease 2019 (COVID-19) appeared in Wuhan city, China. As of April 15, 2020, >1.9 million COVID-19 cases were confirmed worldwide, including >120,000 deaths. There is an urgent need to monitor and predict COVID-19 prevalence to control this spread more effectively. Time series models are significant in predicting the impact of the COVID-19 outbreak and taking the necessary measures to respond to this crisis. In this study, Auto-Regressive Integrated Moving Average (ARIMA) models were developed to predict the epidemiological trend of COVID-19 prevalence of Italy, Spain, and France, the most affected countries of Europe. The prevalence data of COVID-19 from 21 February 2020 to 15 April 2020 were collected from the World Health Organization website. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (0,2,1), ARIMA (1,2,0), and ARIMA (0,2,1) models with the lowest MAPE values (4.7520, 5.8486, and 5.6335) were selected as the best models for Italy, Spain, and France, respectively. This study shows that ARIMA models are suitable for predicting the prevalence of COVID-19 in the future. The results of the analysis can shed light on understanding the trends of the outbreak and give an idea of the epidemiological stage of these regions. Besides, the prediction of COVID-19 prevalence trends of Italy, Spain, and France can help take precautions and policy formulation for this epidemic in other countries.