Evaluating the effects of control interventions and estimating the inapparent infections for dengue outbreak in Hangzhou, China

Evaluating the effectiveness of different intervention measures for an outbreak of mycoplasma pneumoniae in hangzhou based on a dynamic model

For Mycoplasma pneumoniae (MP) infection in schools, the local Center for Disease Control and Prevention recommends nonpharmaceutical interventions, such as case isolation, school closures, suspension of group activities, reinforcement of ventilation and disinfection for influenza outbreaks. However, there is limited evidence supporting and evaluating the effectiveness of these interventions. On the basis of an outbreak of MP infection occurring in a primary school in Zhejiang Province, a susceptible-latent-overt infected-recessive infected-displaced (SEIAR) model was constructed to quantitatively evaluate the prevention and control effects by simulating the intervention measures mentioned above. With no intervention, the outbreak lasted 143 days, and the total attack rate (TAR) and total infection rate (TIR) reached 75.78% and 95.65%, respectively. The most effective single-intervention strategy was ventilation and disinfection (VD), with a TAR as low as 15.81% and a duration of outbreak (DO) of 61 days. The two- or three- combined intervention strategies, including all combinations with 90% VD, were more effective than the single-intervention strategy. In conclusion, the SEIAR model could effectively simulate the epidemic situation of MP and the intervention effect. For the outbreak of MP, the earlier comprehensive measures were taken, such as ventilation and disinfection, and case isolation, the better control effect would be.

Rapid human movement and dengue transmission in Bangladesh: a spatial and temporal analysis based on different policy measures of COVID-19 pandemic and Eid festival

BACKGROUND

Rapid human movement plays a crucial role in the spatial dissemination of the dengue virus. Nevertheless, robust quantification of this relationship using both spatial and temporal models remains necessary. This study aims to explore the spatial and temporal patterns of dengue transmission under various human movement contexts.

METHODS

We obtained district-wise aggregated dengue incidence data from the Management Information System, Directorate General of Health Services of Bangladesh. The stringency index (SI), along with eight individual policy measures (from the Oxford Coronavirus Government Response Tracker database) and six mobility indices (as measured by Google's Community Mobility Reports) were obtained as human movement indicators. A multi-step correlative modelling approach, including various spatial and temporal models, was utilized to explore the associations of dengue incidence with the SI, fourteen human movement indices and the Eid festival.

RESULTS

The global Moran's I indicated significant spatial autocorrelation in dengue incidence during the pre-pandemic (Moran's I: 0.14, P < 0.05) and post-pandemic periods (Moran's I: 0.42, P < 0.01), while the pandemic period (2020-2022) showed weaker, non-significant spatial clustering (Moran's I: 0.07, P > 0.05). Following the pandemic, we identified the emergence of new dengue hotspots. We found a strong negative relationship between monthly dengue incidence and the SI (rspearman: - 0.62, P < 0.01). Through the selection of an optimal Seasonal autoregressive integrated moving average model, we observed that the closure of public transport (β = - 1.66, P < 0.10) and restrictions on internal movement (β = - 2.13, P < 0.10) were associated with the reduction of dengue incidence. Additionally, observed cases were substantially lower than predicted cases during the period from 2020 to 2022. By utilising additional time-series models, we were able to identify in 2023 a rise in dengue incidence associated with the Eid festival intervention, even after adjusting for important climate variables.

CONCLUSIONS

Overall, rapid human movement was found to be associated with increased dengue transmission in Bangladesh. Consequently, the implemention of effective mosquito control interventions prior to large festival periods is necessary for preventing the spread of the disease nationwide. We emphasize the necessity for developing advanced surveillance and monitoring networks to track real-time human movement patterns and dengue incidence.

Assessing the impact of emergency measures in varied population density areas during a large dengue outbreak

Background

The patterns of dengue are affected by many factors, including population density and climate factors. Densely populated areas could play a role in dengue transmission due to increased human-mosquito contacts, the presence of more diverse and suitable vector habitats and breeding sites, and changes in land use. In addition to population densities, climatic factors such as temperature, relative humidity, and precipitation have been demonstrated to predict dengue patterns. To control dengue, emergency measures should focus on vector management. Most approaches to assessing emergency responses to dengue risks involve applying simulation models or describing emergency activities and the results of implementing those responses. Research using real-world data with analytical methods to evaluate emergency responses to dengue has been limited. This study investigated emergency control measures associated with dengue risks in areas with high and low population densities, considering their different control capacities.

Methodology

Data from the 2015 dengue outbreak in Kaohsiung City, Taiwan, were utilized. The government database provided information on confirmed dengue cases, emergency control measures, and climatic data. The study employed a distributed lag non-linear model (DLNM) to assess the effect of emergency control measures and their time lags on dengue risk.

Principal findings

The findings revealed that in areas with high population density, the absence of emergency measures significantly elevated the risks of dengue. However, implementing emergency measures, especially a higher number, was associated with lower risks. In contrast, in areas with low population density, the risks of dengue were only significantly elevated at the 1st week lag if no emergency control measures were implemented. When emergency activities were carried out, the risks of dengue significantly decreased only for the 1st week lag.

Conclusions

Our findings reveal distinct exposure-lag-response patterns in the associations between emergency control measures and dengue in areas with high and low population density. In regions with a high population density, implementing emergency activities during a significant dengue outbreak is crucial for reducing the risk. Conversely, in areas of low population density, the necessity of applying emergency activities may be less pronounced. The implications of this study on dengue management could provide valuable insights for health authorities dealing with limited resources.

Land use and land cover change and its impacts on dengue dynamics in China: A systematic review

BACKGROUND

Dengue is a prioritized public health concern in China. Because of the larger scale, more frequent and wider spatial distribution, the challenge for dengue prevention and control has increased in recent years. While land use and land cover (LULC) change was suggested to be associated with dengue, relevant research has been quite limited. The "Open Door" policy introduced in 1978 led to significant LULC change in China. This systematic review is the first to review the studies on the impacts of LULC change on dengue dynamics in China. This review aims at identifying the research evidence, research gaps and provide insights for future research.

METHODS

A systematic literature review was conducted following the PRISMA protocol. The combinations of search terms on LULC, dengue and its vectors were searched in the databases PubMed, Web of Science, and Baidu Scholar. Research conducted on China published from 1978 to December 2019 and written in English or Chinese was selected for further screening. References listed in articles meeting the inclusion criteria were also reviewed and included if again inclusion criteria were met to minimize the probability of missing relevant research.

RESULTS

28 studies published between 1978 and 2017 were included for the full review. Guangdong Province and southern Taiwan were the major regional foci in the literature. The majority of the reviewed studies observed associations between LULC change factors and dengue incidence and distribution. Conflictive evidence was shown in the studies about the impacts of green space and blue space on dengue in China. Transportation infrastructure and urbanization were repeatedly suggested to be positively associated with dengue incidence and spread. The majority of the studies reviewed considered meteorological and sociodemographic factors when they analyzed the effects of LULC change on dengue. Primary and secondary remote sensing (RS) data were the primary source for LULC variables. In 21 of 28 studies, a geographic information system (GIS) was used to process data of environmental variables and dengue cases and to perform spatial analysis of dengue.

CONCLUSIONS

The effects of LULC change on the dynamics of dengue in China varied in different periods and regions. The application of RS and GIS enriches the means and dimensions to explore the relations between LULC change and dengue. Further comprehensive regional research is necessary to assess the influence of LULC change on local dengue transmission to provide practical advice for dengue prevention and control.

Paradigm Shift in Socio-Demographic Profile of Dengue Infection: A Hospital Based Cross-Sectional Study

Background:

Dengue is the most rapidly spreading mosquito borne viral disease in the world with increasing geographical expansion to new countries and from urban to rural settings due to combination of urbanisation, population growth, increased international travel and trade and global warming. The epidemiology of dengue fever in India has been very complex with a seasonal pattern. The first outbreak in Odisha was in 2010 and is now spreading to different districts of the state.

Materials and Methods:

A hospital based cross-sectional study was carried out between 2017 to 2018 in the dengue ward of a teaching hospital in Berhampur, Ganjam district of Odisha.

Results:

The prevalence of admitted dengue patients was 4.32%. Majority of the patients were males (81.9%) and ≥15 years old (91.7%). About 68.5% were from rural areas and belonged to low socio-economic status (53.2%). Cases were reported mostly in peri-monsoon periods and 65.7% of them came directly to the hospital.

Conclusion:

The study highlights the need to curb the rural spread of the disease through activities in creating awareness among all section of people to promote control measures and early reporting of all fever cases, capacity building of rural doctors for early detection, treatment and early referral of high-risk patients and availability of ELISA based tests in sub-district hospitals along with Rapid Diagnostic Kits (RDKs). Emphasis for preventive and control measures to be increased during peri-monsoon periods and also to be instituted in offices, educational institutes and other indoor activity areas.

A murine model of dengue virus infection in suckling C57BL/6 and BALB/c mice

Dengue is a significant public health concern across tropical and subtropical regions worldwide, principally causing disease in children. Very young children are at increased risk of severe manifestations of dengue infection. The mechanism of dengue disease in this population is not fully understood. In this study, we present a murine model of dengue virus primary infection in suckling C57BL/6 and BALB/c mice in order to investigate disease pathogenesis. Three-day-old C57BL/6 mice intraperitoneally infected with DENV-2 NGC were more susceptible to infection than BALB/c mice, showing increased liver enzymes, extended viremia, dissemination to organs and histological alterations in liver and small intestine. Furthermore, the immune response in DENV-infected C57BL/6 mice exhibited a marked Th1 bias compared to BALB/c mice. These findings highlight the possibility of establishing an immunocompetent mouse model of DENV-2 infection in suckling mice that reproduces certain signs of disease observed in humans and that could be used to further study age-related mechanisms of dengue pathogenesis.

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.