Examining the Effect of the Environment and Commuting Flow from/to Epidemic Areas on the Spread of Dengue Fever

The impact of COVID-19 lockdown measures on gendered mobility patterns in France

The COVID-19 crisis has upset the way of life of our society. The objective of this study was to apprehend the consequences of public health policies on mobility through the lens of gender. The analyses are based on a representative sample of 3000 people living in France. Travel behaviour was quantified using three mobility indicators (number of daily trips, daily distance travelled and daily travel time) that we regressed on individual and contextual explanatory variables. Two periods were studied: lockdown (March 17, 2020 until May 11, 2020), and post-lockdown (a curfew period: January-February 2021). For the lockdown period, our results show: (i) a statistically significant gender difference for the three mobility indicators. On average, women made 1.19 daily trips versus 1.46 for men, travelled 12 km whereas versus 17 km for men and spent less time on travel (23 min) than men (30 min); (ii) the degree of mobility was particularly sensitive to access to a car, according to a gender difference. For the post-lockdown period, our results reveal that: (i) women were more likely than men to make a higher number of daily trips (OR = 1.10, 95% CI = [1.04-1.17]); (ii) having only one or no car in the household impacted the mobility of women during the post-lockdown period; (iii) women regained some mobility but without reaching the pre-lockdown level. A better understanding of the factors influencing mobility behaviour, in lockdown and curfew periods, can provide some pathways to improve transport planning and help public authorities while tackling gender inequalites.

A survey of clinical and laboratory characteristics of the dengue fever epidemic from 2017 to 2019 in Zhejiang, China

To explore the epidemic, clinical, and laboratory characteristics of dengue patients in Zhejiang and the possible mechanism. Epidemic, clinical and laboratory data of 231 dengue patients admitted to the Second Affiliated Hospital of Zhejiang Traditional Chinese Medicine University between August 2017 and December 2019 were collected. GSE43777 dataset was downloaded from the Gene Expression Omnibus database and was used for the immune cell infiltration analysis, logistic regression analysis, and nomogram construction. Gene set enrichment analysis (GSEA) was performed to explore the possible regulatory pathways in dengue infection. Further, the receiver operating characteristic curve analysis and decision curve analysis were conducted to evaluate the value of related immune cells in predicting dengue severity. Among the 231 patients, the gender ratio was 1:1.1 (male/female). The patients in the <60 years age group, 60 to 80 years age group, and >80 years age group were 47.2%, 45.5%, and 7.3%, respectively. The major symptoms were fever (100%), weak (98.3%), anorexia (76.6%), muscle and joint pain (62.3%), and nausea (46.8%). In dengue patients, 98.7% of serum samples had decreased platelet levels, 96.5% of them had decreased white blood cell (WBC) levels, 97.8% had elevated aspartate aminotransferase levels, 82.3% had elevated lactate dehydrogenase levels, 49.4% had increased creatinine levels, and 35.5% had increased creatine kinase levels. Pneumonia, pleural effusion, and bilateral pleural reaction were observed in 16.5%, 8.2%, and 4.8%, respectively of dengue patients. Gallbladder wall roughness and splenomegaly accounted for 6.1% and 4.3% of all cases. Moreover, the levels of T cell, B cell, and dendritic cells were significantly higher in the convalescent group and they were involved in immune- and metabolism-related pathways. Of note, low levels of these 3 immune cells correlated with high dengue infection risk, while only dendritic cells exhibited satisfactory performance in predicting dengue severity. Dengue fever patients often onset with fever, accompanied by mild abnormalities of the blood system and other organ functions. Moreover, T cells, B cells, and dendritic cells might be involved in dengue infection and development.

Influences of Differentiated Residence and Workplace Location on the Identification of Spatiotemporal Patterns of Dengue Epidemics: A Case Study in Guangzhou, China

The location of the infections is the basic data for precise prevention and control of dengue fever (DF). However, most studies default to residence address as the place of infection, ignoring the possibility that cases are infected at other places (e.g., workplace address). This study aimed to explore the spatiotemporal patterns of DF in Guangzhou from 2016 to 2018, differentiating workplace and residence. In terms of temporal and spatial dimensions, a case weight assignment method that differentiates workplace and residence location was proposed, taking into account the onset of cases around their workplace and residence. Logistic modeling was used to classify the epidemic phases. Spatial autocorrelation analysis was used to reveal the high and early incidence areas of DF in Guangzhou from 2016 to 2018. At high temporal resolution, the DF in Guangzhou has apparent phase characteristics and is consistent with logistic growth. The local epidemic is clustered in terms of the number of cases and the time of onset and outbreak. High and early epidemic areas are mainly distributed in the central urban areas of Baiyun, Yuexiu, Liwan and Haizhu districts. The high epidemic areas due to commuting cases can be further identified after considering the workplaces of cases. Improving the temporal resolution and differentiating the workplace and residence address of cases could help to improve the identification of early and high epidemic areas in analyzing the spatiotemporal patterns of dengue fever in Guangzhou, which could more reasonably reflect the spatiotemporal patterns of DF in the study area.

Epidemiology of Indigenous Dengue Cases in Zhejiang Province, Southeast China

Objective

Autochthonous transmission of the dengue virus (DENV) occurred each year from 2014 to 2018 in Zhejiang province, and became an emerging public health problem. We characterized the autochthonous transmission of the DENV and traced the source of infection for further control and prevention of dengue.

Methods

Descriptive and spatiotemporal cluster analyses were conducted to characterize the epidemiology of autochthonous transmission of the DENV. Molecular epidemiology was used to identify the infection source.

Results

In total, 1,654 indigenous cases and 12 outbreaks, with no deaths, were reported during 2004–2018. Before 2017, all outbreaks occurred in suburban areas. During 2017–2018, five out of eight outbreaks occurred in urban areas. The median duration of outbreaks (28 days) in 2017–2018 was shortened significantly (P = 0.028) in comparison with that in 2004–2016 (71 days). The median onset-visiting time, visiting-confirmation time, and onset-confirmation time was 1, 3, and 4 days, respectively. The DENV serotypes responsible for autochthonous transmission in Zhejiang Province were DENV 1, DENV 2, and DENV 3, with DENV 1 being the most frequently reported. Southeast Asia was the predominant source of indigenous infection.

Conclusions

Zhejiang Province witnessed an increase in the frequency, incidence, and geographic expansion of indigenous Dengue cases in recent years. The more developed coastal and central region of Zhejiang Province was impacted the most.

Integrating Spatial Modelling and Space-Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan

The spatial–temporal assessment of vector diseases is imperative to design effective action plans and establish preventive strategies. Therefore, such assessments have potential public health planning-related implications. In this context, we here propose an integrated spatial disease evaluation (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the Kernel Density Estimation, the Optimized Hot Spot Analysis, space–time assessment and prediction, and the Geographically Weighted Regression (GWR). It makes it possible to systematically assess the disease concentrations, patterns/trends, clustering, prediction dynamics, and spatially varying relationships between disease and different associated factors. To demonstrate the applicability and effectiveness of the I-SpaDE, we apply it in the second largest city of Pakistan, namely Lahore, using Dengue Fever (DF) during 2007–2016 as an example vector disease. The most significant clustering is evident during the years 2007–2008, 2010–2011, 2013, and 2016. Mostly, the clusters are found within the city’s central functional area. The prediction analysis shows an inclination of DF distribution from less to more urbanized areas. The results from the GWR show that among various socio-ecological factors, the temperature is the most significantly associated with the DF followed by vegetation and built-up area. While the results are important to understand the DF situation in the study area and have useful implications for public health planning, the proposed framework is flexible, replicable, and robust to be utilized in other similar regions, particularly in developing countries in the tropics and sub-tropics.