A threshold analysis of dengue transmission in terms of weather variables and imported dengue cases in Australia

Genetic and structural characterization of dengue virus involved in the 2023 autochthonous outbreaks in central Italy

Dengue virus (DENV) has been expanding its range to temperate areas that are not usually affected, where the spread of vectors has been facilitated by global trade and climate change. In Europe, there have been many cases of DENV imported from other regions in the past few years, leading to local outbreaks of DENV among people without travel history. Here we describe the epidemiological and molecular investigations of three transmission events locally acquired DENV infections caused by serotypes 1, 2 and 3, respectively, in the Latium Region from August to November 2023. Next-generation or Sanger sequencing was used to obtain the whole genomes, or the complete E-gene of the viruses, respectively. The structure of the DENV-1 and DENV-3 sequences was analysed to identify amino acid changes that were not found in the closest related sequences. The major cluster was supported by DENV-1 (originated in South America), with 42 autochthonous infections almost occurring in the eastern area of Rome, probably due to a single introduction followed by local sustained transmission. Seven DENV-1 subclusters have been identified by mutational and phylogenetic analysis. Structural analysis indicated changes whose meaning can be explained by the adaptation of the virus to human hosts and vectors and their interactions with antibodies and cell receptors.

Arbovirus Transmission in Australia from 2002 to 2017

Arboviruses pose a significant global public health threat, with Ross River virus (RRV), Barmah Forest virus (BFV), and dengue virus (DENV) being among the most common and clinically significant in Australia. Some arboviruses, including those prevalent in Australia, have been reported to cause transfusion-transmitted infections. This study examined the spatiotemporal variation of these arboviruses and their potential impact on blood donation numbers across Australia. Using data from the Australian Department of Health on eight arboviruses from 2002 to 2017, we retrospectively assessed the distribution and clustering of incidence rates in space and time using Geographic Information System mapping and space-time scan statistics. Regression models were used to investigate how weather variables, their lag months, space, and time affect case and blood donation counts. The predictors' importance varied with the spatial scale of analysis. Key predictors were average rainfall, minimum temperature, daily temperature variation, and relative humidity. Blood donation number was significantly associated with the incidence rate of all viruses and its interaction with local transmission of DENV, overall. This study, the first to cover eight clinically relevant arboviruses at a fine geographical level in Australia, identifies regions at risk for transmission and provides valuable insights for public health intervention.

Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore

BACKGROUND

Predictive models can serve as early warning systems and can be used to forecast future risk of various infectious diseases. Conventionally, regression and time series models are used to forecast dengue incidence, using dengue surveillance (e.g., case counts) and weather data. However, these models may be limited in terms of model assumptions and the number of predictors that can be included. Machine learning (ML) methods are designed to work with a large number of predictors and thus offer an appealing alternative. Here, we compared the performance of ML algorithms with that of regression models in predicting dengue cases and outbreaks from 4 to up to 12 weeks in advance. Many countries lack sufficient health surveillance infrastructure, as such we evaluated the contribution of dengue surveillance and weather data on the predictive power of these models.

METHODS

We developed ML, regression, and time series models to forecast weekly dengue case counts and outbreaks in Iquitos, Peru; San Juan, Puerto Rico; and Singapore from 1990-2016. Forecasts were generated using available weekly dengue surveillance, and weather data. We evaluated the agreement between model forecasts and actual dengue observations using Mean Absolute Error and Matthew's Correlation Coefficient (MCC).

RESULTS

For near term predictions of weekly case counts and when using surveillance data, ML models had 21% and 33% less error than regression and time series models respectively. However, using weather data only, ML models did not demonstrate a practical advantage. When forecasting weekly dengue outbreaks 12 weeks in advance, ML models achieved a maximum MCC of 0.61.

CONCLUSIONS

Our results identified 2 scenarios when ML models are advantageous over regression model: 1) predicting dengue weekly case counts 4 weeks ahead when dengue surveillance data are available and 2) predicting weekly dengue outbreaks 12 weeks ahead when dengue surveillance data are unavailable. Given the advantages of ML models, dengue early warning systems may be improved by the inclusion of these models.

Spatial and temporal analysis of dengue infections in Queensland, Australia: Recent trend and perspectives

Dengue is a public health concern in northern Queensland, Australia. This study aimed to explore spatial and temporal characteristics of dengue cases in Queensland, and to identify high-risk areas after a 2009 dengue outbreak at fine spatial scale and thereby help in planning resource allocation for dengue control measures. Notifications of dengue cases for Queensland at Statistical Local Area (SLA) level were obtained from Queensland Health for the period 2010 to 2015. Spatial and temporal analysis was performed, including plotting of seasonal distribution and decomposition of cases, using regression models and creating choropleth maps of cumulative incidence. Both the space-time scan statistic (SaTScan) and Geographical Information System (GIS) were used to identify and visualise the space-time clusters of dengue cases at SLA level. A total of 1,773 dengue cases with 632 (35.65%) autochthonous cases and 1,141 (64.35%) overseas acquired cases were satisfied for the analysis in Queensland during the study period. Both autochthonous and overseas acquired cases occurred more frequently in autumn and showed a geographically expanding trend over the study period. The most likely cluster of autochthonous cases (Relative Risk, RR = 54.52, p<0.001) contained 50 SLAs in the north-east region of the state around Cairns occurred during 2013-2015. A cluster of overseas cases (RR of 60.81, p<0.001) occurred in a suburb of Brisbane during 2012 to 2013. These results show a clear spatiotemporal trend of recent dengue cases in Queensland, providing evidence in directing future investigations on risk factors of this disease and effective interventions in the high-risk areas.

Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore

BACKGROUND

Rainfall patterns are one of the main drivers of dengue transmission as mosquitoes require standing water to reproduce. However, excess rainfall can be disruptive to the Aedes reproductive cycle by "flushing out" aquatic stages from breeding sites. We developed models to predict the occurrence of such "flushing" events from rainfall data and to evaluate the effect of flushing on dengue outbreak risk in Singapore between 2000 and 2016.

METHODS

We used machine learning and regression models to predict days with "flushing" in the dataset based on entomological and corresponding rainfall observations collected in Singapore. We used a distributed lag nonlinear logistic regression model to estimate the association between the number of flushing events per week and the risk of a dengue outbreak.

RESULTS

Days with flushing were identified through the developed logistic regression model based on entomological data (test set accuracy = 92%). Predictions were based upon the aggregate number of thresholds indicating unusually rainy conditions over multiple weeks. We observed a statistically significant reduction in dengue outbreak risk one to six weeks after flushing events occurred. For weeks with five or more flushing events, compared with weeks with no flushing events, the risk of a dengue outbreak in the subsequent weeks was reduced by 16% to 70%.

CONCLUSIONS

We have developed a high accuracy predictive model associating temporal rainfall patterns with flushing conditions. Using predicted flushing events, we have demonstrated a statistically significant reduction in dengue outbreak risk following flushing, with the time lag well aligned with time of mosquito development from larvae and infection transmission. Vector control programs should consider the effects of hydrological conditions in endemic areas on dengue transmission.

Predicting Infectious Disease Using Deep Learning and Big Data

Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study's models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society.

Different responses of weather factors on hand, foot and mouth disease in three different climate areas of Gansu, China

Background

To determine the linear and non-linear interacting relationships between weather factors and hand, foot and mouth disease (HFMD) in children in Gansu, China, and gain further traction as an early warning signal based on weather variability for HFMD transmission.

Method

Weekly HFMD cases aged less than 15 and meteorological information from 2010 to 2014 in Jiuquan, Lanzhou and Tianshu, Gansu, China were collected. Generalized linear regression models (GLM) with Poisson link and classification and regression trees (CART) were employed to determine the combined and interactive relationship of weather factors and HFMD in both linear and non-linear ways.

Results

GLM suggested an increase in weekly HFMD of 5.9% [95% confidence interval (CI): 5.4%, 6.5%] in Tianshui, 2.8% [2.5%, 3.1%] in Lanzhou and 1.8% [1.4%, 2.2%] in Jiuquan in association with a 1 °C increase in average temperature, respectively. And 1% increase of relative humidity could increase weekly HFMD of 2.47% [2.23%, 2.71%] in Lanzhou and 1.11% [0.72%, 1.51%] in Tianshui. CART revealed that average temperature and relative humidity were the first two important determinants, and their threshold values for average temperature deceased from 20 °C of Jiuquan to 16 °C in Tianshui; and for relative humidity, threshold values increased from 38% of Jiuquan to 65% of Tianshui.

Conclusion

Average temperature was the primary weather factor in three areas, more sensitive in southeast Tianshui, compared with northwest Jiuquan; Relative humidity’s effect on HFMD showed a non-linear interacting relationship with average temperature.

Detection of Dengue Virus in Bat Flies (Diptera: Streblidae) of Common Vampire Bats, Desmodus rotundus, in Progreso, Hidalgo, Mexico

Blood-feeding arthropods play a major role in the transmission of several flaviviruses, which represent an important problem for human health. Currently, dengue is one of the most important arboviral emerging diseases worldwide. Furthermore, some previous studies have reported the presence of viral nucleic acids and antibodies against dengue virus (DENV) in wild animals. Our knowledge of the role played by wildlife reservoirs in the sylvatic transmission and maintenance of DENV remains limited. Our objective was to screen blood-feeding ectoparasites (bat flies) and their common vampire bat (Desmodus rotundus) hosts, for flaviviruses in Hidalgo, Mexico. We detected Flavivirus sequences in 38 pools of ectoparasites (Diptera: Streblidae, Strebla wiedemanni and Trichobius parasiticus) and 8 tissue samples of D. rotundus by RT-PCR and semi-nested PCR using FlaviPF1S, FlaviPR2bis, and FlaviPF3S primers specific for NS5, a gene highly conserved among flaviviruses. Phylogenetic inference analysis performed using the maximum likelihood algorithm implemented in PhyML showed that six sequences clustered with DENV (bootstrap value = 53.5%). Although this study supports other reports of DENV detection in bats and arthropods other than Aedes mosquitoes, the role of these ectoparasitic flies and of hematophagous bats in the epidemiology of DENV still warrants further investigation.

Modelling dengue fever risk in the State of Yucatan, Mexico using regional-scale satellite-derived sea surface temperature

Accurately predicting vector-borne diseases, such as dengue fever, is essential for communities worldwide. Changes in environmental parameters such as precipitation, air temperature, and humidity are known to influence dengue fever dynamics. Furthermore, previous studies have shown how oceanographic variables, such as El Niño Southern Oscillation (ENSO)-related sea surface temperature from the Pacific Ocean, influences dengue fever in the Americas. However, literature is lacking on the use of regional-scale satellite-derived sea surface temperature (SST) to assess its relationship with dengue fever in coastal areas. Data on confirmed dengue cases, demographics, precipitation, and air temperature were collected. Incidence of weekly dengue cases was examined. Stepwise multiple regression analyses (AIC model selection) were used to assess which environmental variables best explained increased dengue incidence rates. SST, minimum air temperature, precipitation, and humidity substantially explained 42% of the observed variation (r²=0.42). Infectious diseases are characterized by the influence of past cases on current cases and results show that previous dengue cases alone explained 89% of the variation. Ordinary least-squares analyses showed a positive trend of 0.20±0.03°C in SST from 2006 to 2015. An important element of this study is to help develop strategic recommendations for public health officials in Mexico by providing a simple early warning capability for dengue incidence.

Bayesian dynamic modeling of time series of dengue disease case counts

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.

Using Baidu Search Index to Predict Dengue Outbreak in China

This study identified the possible threshold to predict dengue fever (DF) outbreaks using Baidu Search Index (BSI). Time-series classification and regression tree models based on BSI were used to develop a predictive model for DF outbreak in Guangzhou and Zhongshan, China. In the regression tree models, the mean autochthonous DF incidence rate increased approximately 30-fold in Guangzhou when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 382. When the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 91.8, there was approximately 9-fold increase of the mean autochthonous DF incidence rate in Zhongshan. In the classification tree models, the results showed that when the weekly BSI for DF at the lagged moving average of 1-3 weeks was more than 99.3, there was 89.28% chance of DF outbreak in Guangzhou, while, in Zhongshan, when the weekly BSI for DF at the lagged moving average of 1-5 weeks was more than 68.1, the chance of DF outbreak rose up to 100%. The study indicated that less cost internet-based surveillance systems can be the valuable complement to traditional DF surveillance in China.

El Niño-Southern Oscillation, local weather and occurrences of dengue virus serotypes

Severe dengue fever is usually associated with secondary infection by a dengue virus (DENV) serotype (1 to 4) that is different to the serotype of the primary infection. Dengue outbreaks only occur following importations of DENV in Cairns, Australia. However, the majority of imported cases do not result in autochthonous transmission in Cairns. Although DENV transmission is strongly associated with the El Niño-Southern Oscillation (ENSO) climate cycle and local weather conditions, the frequency and potential risk factors of infections with the different DENV serotypes, including whether or not they differ, is unknown. This study used a classification tree model to identify the hierarchical interactions between Southern Oscillation Index (SOI), local weather factors, the presence of imported serotypes and the occurrence of the four autochthonous DENV serotypes from January 2000–December 2009 in Cairns. We found that the 12-week moving average of SOI and the 2-week moving average of maximum temperature were the most important factors influencing the variation in the weekly occurrence of the four DENV serotypes, the likelihoods of the occurrence of the four DENV serotypes may be unequal under the same environmental conditions, and occurrence may be influenced by changes in global and local environmental conditions in Cairns.