Objectives: To provide an overview of the three major deadly coronaviruses and identify areas for improvement of future preparedness plans, as well as provide a critical assessment of the risk factors and actionable items for stopping their spread, utilizing lessons learned from the first two deadly coronavirus outbreaks, as well as initial reports from the current novel coronavirus (COVID-19) epidemic in Wuhan, China. Methods: Utilizing the Centers for Disease Control and Prevention (CDC, USA) website, and a comprehensive review of PubMed literature, we obtained information regarding clinical signs and symptoms, treatment and diagnosis, transmission methods, protection methods and risk factors for Middle East respiratory syndrome (MERS), severe acute respiratory syndrome (SARS) and COVID-19. Comparisons between the viruses were made. Results: Inadequate risk assessment regarding the urgency of the situation, and limited reporting on the virus within China has, in part, led to the rapid spread of COVID-19 throughout mainland China and into proximal and distant countries. Compared with SARS and MERS, COVID-19 has spread more rapidly, due in part to increased globalization and the focus of the epidemic. Wuhan, China is a large hub connecting the North, South, East and West of China via railways and a major international airport. The availability of connecting flights, the timing of the outbreak during the Chinese (Lunar) New Year, and the massive rail transit hub located in Wuhan has enabled the virus to perforate throughout China, and eventually, globally. Conclusions: We conclude that we did not learn from the two prior epidemics of coronavirus and were ill-prepared to deal with the challenges the COVID-19 epidemic has posed. Future research should attempt to address the uses and implications of internet of things (IoT) technologies for mapping the spread of infection.

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.

Mapping the spatial distribution of the dengue vector Aedes (Ae.) aegypti and accurately predicting its abundance are crucial for designing effective vector control strategies and early warning tools for dengue epidemic prevention. Socio-ecological and landscape factors influence Ae. aegypti abundance. Therefore, we aimed to map the spatial distribution of female adult Ae. aegypti and predict its abundance in northeastern Thailand based on socioeconomic, climate change, and dengue knowledge, attitude and practices (KAP) and/or landscape factors using machine learning (ML)-based system. A total of 1066 females adult Ae. aegypti were collected from four villages in northeastern Thailand during January–December 2019. Information on household socioeconomics, KAP regarding climate change and dengue, and satellite-based landscape data were also acquired. Geographic information systems (GIS) were used to map the household-based spatial distribution of female adult Ae. aegypti abundance (high/low). Five popular supervised learning models, logistic regression (LR), support vector machine (SVM), k-nearest neighbor (kNN), artificial neural network (ANN), and random forest (RF), were used to predict females adult Ae. aegypti abundance (high/low). The predictive accuracy of each modeling technique was calculated and evaluated. Important variables for predicting female adult Ae. aegypti abundance were also identified using the best-fitted model. Urban areas had higher abundance of female adult Ae. aegypti compared to rural areas. Overall, study respondents in both urban and rural areas had inadequate KAP regarding climate change and dengue. The average landscape factors per household in urban areas were rice crop (47.4%), natural tree cover (17.8%), built-up area (13.2%), permanent wetlands (21.2%), and rubber plantation (0%), and the corresponding figures for rural areas were 12.1, 2.0, 38.7, 40.1 and 0.1% respectively. Among all assessed models, RF showed the best prediction performance (socioeconomics: area under curve, AUC = 0.93, classification accuracy, CA = 0.86, F1 score = 0.85; KAP: AUC = 0.95, CA = 0.92, F1 = 0.90; landscape: AUC = 0.96, CA = 0.89, F1 = 0.87) for female adult Ae. aegypti abundance. The combined influences of all factors further improved the predictive accuracy in RF model (socioeconomics + KAP + landscape: AUC = 0.99, CA = 0.96 and F1 = 0.95). Dengue prevention practices were shown to be the most important predictor in the RF model for female adult Ae. aegypti abundance in northeastern Thailand. The RF model is more suitable for the prediction of Ae. aegypti abundance in northeastern Thailand. Our study exemplifies that the application of GIS and machine learning systems has significant potential for understanding the spatial distribution of dengue vectors and predicting its abundance. The study findings might help optimize vector control strategies, future mosquito suppression, prediction and control strategies of epidemic arboviral diseases (dengue, chikungunya, and Zika). Such strategies can be incorporated into One Health approaches applying transdisciplinary approaches considering human-vector and agro-environmental interrelationships.

Dengue is linked with climate change in tropical and sub-tropical countries including the Lao People's Democratic Republic (Laos) and Thailand. Knowledge about these issues and preventive measures can affect the incidence and outbreak risk of dengue. Therefore, the present study was conducted to determine the knowledge, attitudes, and practices (KAP) among urban and rural communities and government officials about climate change and dengue in Laos and Thailand. A cross-sectional KAP survey about climate change and dengue were conducted in 360 households in Laos (180 urban and 180 rural), 359 households in Thailand (179 urban and 180 rural), and 20 government officials (10 in each country) using structured questionnaires. Data analysis was undertaken using descriptive methods, principal component analysis (PCA), Chi-square test or Fisher's exact test (as appropriate), and logistic regression. Significant differences among the selected communities in both countries were found in terms of household participant's age, level of education, socioeconomic status, attitude level of climate change and KAP level of dengue (P < 0.05; 95% CI). Overall, participants' KAP about climate change and dengue were low except the attitude level for dengue in both countries. The level of awareness among government officials regarding the climatic relationship with dengue was also low. In Lao households, participants' knowledge about climate change and dengue was significantly associated with the level of education and socioeconomic status (SES) (P < 0.01). Their attitudes towards climate change and dengue were associated with educational level and internet use (P < 0.05). Householders' climate change related practices were associated with SES (P < 0.01) and dengue related practices were associated with educational level, SES, previous dengue experience and internet use (P < 0.01). In Thailand, participants' knowledge about climate change was associated with the level of education and SES (P < 0.01). Their attitudes towards climate change were associated with residence status (urban/rural) and internet use (P < 0.05); climate change related practices were associated with educational level and SES (P < 0.05). Dengue related knowledge of participants was associated with SES and previous dengue experience (P < 0.05); participants' dengue related attitudes and practices were associated with educational level (P < 0.01). The findings call for urgently needed integrated awareness programs to increase KAP levels regarding climate change adaptation, mitigation and dengue prevention to improve the health and welfare of people in these two countries, and similar dengue-endemic countries.

Background: Bangladesh experienced its worst dengue fever (DF) outbreak in 2019. This study investigated the knowledge, attitudes and practices (KAP) among university students in Bangladesh and significant factors associated with their prevention practices related to climate change and DF. Methods: A social media–based (Facebook) cross-sectional KAP survey was conducted and secondary data of reported DF cases in 2019 extracted. Logistic regression and spatial analysis were run to examine the data. Results: Of 1500 respondents, 76% believed that climate change can affect DF transmission. However, participants reported good climate change knowledge (76.7%), attitudes (87.9%) and practices (39.1%). The corresponding figures for DF were knowledge (47.9%), attitudes (80.3%) and practices (25.9%). Good knowledge and attitudes were significantly associated with good climate change adaptation or mitigation practices (p<0.05). Good knowledge, attitudes and previous DF experiences were also found to be significantly associated with good DF prevention practices (p<0.001). There was no significant positive correlation between climate change and DF KAP scores and the number of DF cases. Conclusions: Findings from this study provide baseline data that can be used to promote educational campaigns and intervention programs focusing on climate change adaptation and mitigation and effective DF prevention strategies among various communities in Bangladesh and similar dengue-endemic countries.

Digital technologies are the need of today to predict, prevent and control emerging infectious diseases. Bangladesh is one of the world's poorest and most densely populated countries and faces a double burden of two deadly diseases, COVID-19 and dengue. In response to both these diseases, the absence of a digital healthcare system and insufficient preparedness, lack of public awareness pose unique challenges and a large threat to the population, resulting in epidemics of escalating severity. This paper suggests a digital health care and surveillance system based on the internet of things (IoT) and artificial intelligence (AI) for timely identification of COVID-19 and dengue cases and improving the prevention and control strategies in the country.