Spatiotemporal Distribution and Geospatial Diffusion Patterns of 2013 Dengue Outbreak in Jhapa District, Nepal

High-Risk Areas for Congenital Zika Syndrome in Rio de Janeiro: Spatial Cluster Detection

Brazil reported 18,282 suspected congenital Zika syndrome (CZS) cases up to 2018 and accounts for 61.4% of the total reported Zika cases in the Americas in the period. To detect high-risk areas for children with CZS in the city of Rio de Janeiro, we used cluster detection and thematic maps. We analyzed data using a Poisson model in Satscan 10.1.3 software. We also analyzed the records of children with CZS from 2015 to 2016 to describe the clinical and epidemiological maternal and child profile, as well as live births in 2016 and the social development index (SDI) by neighborhood. In 2015 and 2016, the incidence rates of CZS were 8.84 and 46.96 per 100,000 live births in the city, respectively. Severe congenital findings such as microcephaly and brain damage, osteoarticular impairment, ocular abnormalities, and hearing loss were observed in 47 children. The spatial distribution of CZS was concentrated in the north and west zones in heterogeneous neighborhoods. The neighborhoods with the highest occurrence of CZS cases were found to have the worst SDIs. Stascan detected three spatial clusters in the north zone, where the SDI is lower. The clusters presented high relative risks for CZS (7.86, 1.46, and 2.08), although they were not statistically significant. Our findings highlight a higher occurrence of CZS in areas with less favorable socioeconomic conditions.

Co-Circulation of Dengue Virus Serotypes 1, 2, and 3 during the 2022 Dengue Outbreak in Nepal: A Cross-Sectional Study

The largest dengue outbreak in the history of Nepal occurred in 2022, with a significant number of casualties. It affected all 77 districts, with the nation's capital, Kathmandu (altitude 1300 m), being the hardest hit. However, the molecular epidemiology of this outbreak, including the dengue virus (DENV) serotype(s) responsible for this epidemic, remain unknown. Here, we report the epidemic trends, clinico-laboratory features, and virus serotypes and their viral load profiles that are associated with this outbreak in Nepal. Dengue-suspected febrile patients were investigated by routine laboratory, serological, and molecular tools, including a real-time quantitative polymerase chain reaction (qRT-PCR). Of the 538 dengue-suspected patients enrolled, 401 (74.5%) were diagnosed with dengue. Among these dengue cases, 129 (32.2%) patients who required hospital admission had significant associations with myalgia, rash, diarrhea, retro-orbital pain, bleeding, and abdominal pain. DENV-1, -2, and -3 were identified during the 2022 epidemic, with a predominance of DENV-1 (57.1%) and DENV-3 (32.1%), exhibiting a new serotype addition. We found that multiple serotypes circulated in 2022, with a higher frequency of hospitalizations, more severe dengue, and more deaths than in the past. Therefore, precise mapping of dengue and other related infections through integrated disease surveillance, evaluation of the dynamics of population-level immunity and virus evolution should be the urgent plans of action for evidence-based policy-making for dengue control and prevention in the country.

Spatio-temporal and trade export risk analysis of bluetongue disease in France: A case study of China

Bluetongue disease (BT) is a viral disease that can be introduced through imported animals and animal products, affecting local animal husbandry. In this study, the spatial and temporal patterns of BT outbreaks (outbreak: a BT infection in cattle, sheep, or goats on a farm, involving at least one infected animal) in France were analyzed and the risk of introducing bluetongue virus (BTV) into countries through trade was assessed. A spatiotemporal analysis of BT reported during the study period (2015-2018) showed that there were clustered outbreaks of BT in France in 2016 and 2017, with outbreaks concentrated from August to December. The outbreak moved eastward from the center of mainland France to surrounding countries. A semi-quantitative risk analysis framework was established by combining the likelihood assessment and consequence analysis of introducing BTV into trading countries through trade. Exemplified by China, the research showed that in the analysis of the likelihood of BTV from France being introduced into trading countries through live cattle trade, China imports a large number of live cattle, bringing high risks. The likelihood of introducing bovine semen into trading countries was similar to that of live cattle, but the harm caused by the trade in live cattle was higher than that caused by the trade in bovine semen. This risk analysis framework can provide a reference for other countries to quickly assess the risk of bluetongue transmission in import and export trade.

The knowledge, attitude and practice of community people on dengue fever in Central Nepal: a cross-sectional study

Background

Since 2006, Nepal has experienced frequent Dengue fever (DF) outbreaks. Up to now, there have been no knowledge, attitude and practice (KAP) studies carried out on DF in Nepal that have included qualitative in-depth and quantitative data. Thus, we aimed to explore and compare the KAP of people residing in the lowland (< 1500 m) and highland (> 1500 m) areas of Nepal.

Methods

A cross-sectional mixed-method study was conducted in six districts of central Nepal in September–October 2018 including both quantitative (660 household surveys) and qualitative data (12 focus group discussions and 27 in-depth interviews). The KAP assessment was executed using a scoring system and defined as high or low based on 80% cut-off point. Logistic regression was used to investigate the associated factors, in quantitative analysis. The deductive followed by inductive approach was adopted to identify the themes in the qualitative data.

Results

The study revealed that both the awareness about DF and prevention measures were low. Among the surveyed participants, 40.6% had previously heard about DF with a significantly higher number in the lowland areas. Similarly, IDI and FGD participants from the lowland areas were aware about DF, and it’s associated symptoms, hence they were adopting better preventive practices against DF. The findings of both the qualitative and quantitative data indicate that people residing in the lowland areas had better knowledge on DF compared to people in highland areas. All IDI participants perceived a higher chance of increasing future dengue outbreaks due to increasing temperature and the mobility of infected people from endemic to non-endemic areas. The most quoted sources of information were the television (71.8%) and radio (51.5%). Overall, only 2.3% of the HHS participants obtained high knowledge scores, 74.1% obtained high attitude scores and 21.2% obtained high preventive practice scores on DF. Among the socio-demographic variables, the area of residence, educational level, age, monthly income, SES and occupation were independent predictors of knowledge level, while the education level of the participants was an independent predictor of the attitude level.

Conclusions

Our study found a very low level of knowledge and insufficient preventive practices. This highlights an urgent need for extensive dengue prevention programs in both highland and lowland communities of Nepal.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07404-4.

Awareness and perception of malaria and dengue at school and college level in the district of Multan

The purpose of this study is to examine the awareness and perception of malaria and dengue fever in Multan Punjab, Pakistan while taking into account the important role of government policies and other variables. The goal of this study is to examine the awareness of students in Multan, Pakistan on malaria and dengue. This study is based on a quantitative approach of secondary evidence from scientific journals and questionnaire surveys. It is also based on observational evidence gathered in Multan Punjab Pakistan, in a field study. The survey with school children, teachers and healthcare professionals were both formal and semi-structuralize. Studies have found that malaria and dengue mainly affect children's schooling through their absence, but can also induce brain loss and cognitive disability. In questionnaires, students were seen to have different understanding of the illness, but also to be able to serve as agents of health reform only through teachers. A sample size of 500 respondents has been selected from different colleges of district Multan Punjab, Pakistan. Correlation technique is used for the data analysis. According to our results it is concluded that the students at college level are aware of malaria and dengue diseases, but they are not capable of engaging and serving as agents for health reform. On the basis of results it is recommended that students must teach about epidemics diseases regarding how to handle these diseases.

Measuring of the COVID-19 Based on Time-Geography

At the end of 2019, the COVID-19 pandemic began to emerge on a global scale, including China, and left deep traces on all societies. The spread of this virus shows remarkable temporal and spatial characteristics. Therefore, analyzing and visualizing the characteristics of the COVID-19 pandemic are relevant to the current pressing need and have realistic significance. In this article, we constructed a new model based on time-geography to analyze the movement pattern of COVID-19 in Hebei Province. The results show that as time changed COVID-19 presented an obvious dynamic distribution in space. It gradually migrated from the southwest region of Hebei Province to the northeast region. The factors affecting the moving patterns may be the migration and flow of population between and within the province, the economic development level and the development of road traffic of each city. It can be divided into three stages in terms of time. The first stage is the gradual spread of the epidemic, the second is the full spread of the epidemic, and the third is the time and again of the epidemic. Finally, we can verify the accuracy of the model through the standard deviation ellipse and location entropy.

Spatiotemporal Distribution of Dengue and Chikungunya in the Hindu Kush Himalayan Region: A Systematic Review

The risk of increasing dengue (DEN) and chikungunya (CHIK) epidemics impacts 240 million people, health systems, and the economy in the Hindu Kush Himalayan (HKH) region. The aim of this systematic review is to monitor trends in the distribution and spread of DEN/CHIK over time and geographically for future reliable vector and disease control in the HKH region. We conducted a systematic review of the literature on the spatiotemporal distribution of DEN/CHIK in HKH published up to 23 January 2020, following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. In total, we found 61 articles that focused on the spatial and temporal distribution of 72,715 DEN and 2334 CHIK cases in the HKH region from 1951 to 2020. DEN incidence occurs in seven HKH countries, i.e., India, Nepal, Bhutan, Pakistan, Bangladesh, Afghanistan, and Myanmar, and CHIK occurs in four HKH countries, i.e., India, Nepal, Bhutan, and Myanmar, out of eight HKH countries. DEN is highly seasonal and starts with the onset of the monsoon (July in India and June in Nepal) and with the onset of spring (May in Bhutan) and peaks in the postmonsoon season (September to November). This current trend of increasing numbers of both diseases in many countries of the HKH region requires coordination of response efforts to prevent and control the future expansion of those vector-borne diseases to nonendemic areas, across national borders.

A dengue fever predicting model based on Baidu search index data and climate data in South China

With the acceleration of global urbanization and climate change, dengue fever is spreading worldwide. Different levels of dengue fever have also occurred in China, especially in southern China, causing enormous economic losses. Unfortunately, there is no effective treatment for dengue, and the most popular dengue vaccine does not exhibit good curative effects. Therefore, we developed a Generalized Additive Mixed Model (GAMM) that gathered climate factors (mean temperature, relative humidity and precipitation) and Baidu search data during 2011-2015 in Guangzhou city to improve the accuracy of dengue fever prediction. Firstly, the time series dengue fever data were decomposed into seasonal, trend and remainder components by the seasonal-trend decomposition procedure based on loess (STL). Secondly, the time lag of variables was determined in cross-correlation analysis and the order of autocorrelation was estimated using autocorrelation (ACF) and partial autocorrelation functions (PACF). Finally, the GAMM was built and evaluated by comparing it with Generalized Additive Mode (GAM). Experimental results indicated that the GAMM (R2: 0.95 and RMSE: 34.1) has a superior prediction capability than GAM (R2: 0.86 and RMSE: 121.9). The study could help the government agencies and hospitals respond early to dengue fever outbreak.

Modeling the spatially varying risk factors of dengue fever in Jhapa district, Nepal, using the semi-parametric geographically weighted regression model

Dengue fever is expanding rapidly in many tropical and subtropical countries since the last few decades. However, due to limited research, little is known about the spatial patterns and associated risk factors on a local scale particularly in the newly emerged areas. In this study, we explored spatial patterns and evaluated associated potential environmental and socioeconomic risk factors in the distribution of dengue fever incidence in Jhapa district, Nepal. Global and local Moran's I were used to assess global and local clustering patterns of the disease. The ordinary least square (OLS), geographically weighted regression (GWR), and semi-parametric geographically weighted regression (s-GWR) models were compared to describe spatial relationship of potential environmental and socioeconomic risk factors with dengue incidence. Our result revealed heterogeneous and highly clustered distribution of dengue incidence in Jhapa district during the study period. The s-GWR model best explained the spatial association of potential risk factors with dengue incidence and was used to produce the predictive map. The statistical relationship between dengue incidence and proportion of urban area, proximity to road, and population density varied significantly among the wards while the associations of land surface temperature (LST) and normalized difference vegetation index (NDVI) remained constant spatially showing importance of mixed geographical modeling approach (s-GWR) in the spatial distribution of dengue fever. This finding could be used in the formulation and execution of evidence-based dengue control and management program to allocate scare resources locally.