Making Vector-Borne Disease Surveillance Work: New Opportunities From the SDG Perspectives

Holistic One Health Surveillance Framework: Synergizing Environmental, Animal, and Human Determinants for Enhanced Infectious Disease Management

Recent pandemics, including the COVID-19 outbreak, have brought up growing concerns about transmission of zoonotic diseases from animals to humans. This highlights the requirement for a novel approach to discern and address the escalating health threats. The One Health paradigm has been developed as a responsive strategy to confront forthcoming outbreaks through early warning, highlighting the interconnectedness of humans, animals, and their environment. The system employs several innovative methods such as the use of advanced technology, global collaboration, and data-driven decision-making to come up with an extraordinary solution for improving worldwide disease responses. This Review deliberates environmental, animal, and human factors that influence disease risk, analyzes the challenges and advantages inherent in using the One Health surveillance system, and demonstrates how these can be empowered by Big Data and Artificial Intelligence. The Holistic One Health Surveillance Framework presented herein holds the potential to revolutionize our capacity to monitor, understand, and mitigate the impact of infectious diseases on global populations.

Solid waste management and Aedes aegypti infestation interconnections: A regression tree application

Public health is at the core of all environmental and anthropic impacts. Urban and territorial planners should include public health concerns in their plans. Basic sanitation infrastructure is essential to maintaining public health and social and economic development. This infrastructure deficiency causes diseases, death and economic losses in developing countries. Framing interconnections among health, sanitation, urbanization and circular economy will assist sustainable development goal achievements. This study aims to identify the relationships between solid waste management indicators in Brazil and the Aedes aegypti mosquito infestation index. Regression trees were employed for modelling due to the complexity and characteristics of the data. The analyses were performed separately from data collected from 3501 municipalities and 42 indicators from the country's five regions. Results show that expenses and personnel indicators were the most critical indicators (in the mid-western, southeastern and southern regions), operational (northeastern (NE) region) and management (northern region). The mean absolute errors ranged from 0.803 (southern region) to 2.507 (NE region). Regional analyses indicate that the municipalities with better SWM results display lower infestation rates in buildings and residences. This research is innovative as it analyses infestation rates rather than dengue prevalence, using a machine learning method, in a multidisciplinary research field that needs further study.

Digital and technological innovation in vector-borne disease surveillance to predict, detect, and control climate-driven outbreaks

Vector-borne diseases are particularly sensitive to changes in weather and climate. Timely warnings from surveillance systems can help to detect and control outbreaks of infectious disease, facilitate effective management of finite resources, and contribute to knowledge generation, response planning, and resource prioritisation in the long term, which can mitigate future outbreaks. Technological and digital innovations have enabled the incorporation of climatic data into surveillance systems, enhancing their capacity to predict trends in outbreak prevalence and location. Advance notice of the risk of an outbreak empowers decision makers and communities to scale up prevention and preparedness interventions and redirect resources for outbreak responses. In this Viewpoint, we outline important considerations in the advent of new technologies in disease surveillance, including the sustainability of innovation in the long term and the fundamental obligation to ensure that the communities that are affected by the disease are involved in the design of the technology and directly benefit from its application.

Protective effect of house screening against indoor Aedes aegypti in Mérida, Mexico: A cluster randomised controlled trial

OBJECTIVE

To evaluate the protective effect of house screening (HS) on indoor Aedes aegypti infestation, abundance and arboviral infection in Merida, Mexico.

METHODS

In 2019, we performed a cluster randomised controlled trial (6 control and 6 intervention areas: 100 households/area). Intervention clusters received permanently fixed fiberglass HS on all windows and doors. The study included two cross-sectional entomologic surveys, one baseline (dry season in May 2019) and one post-intervention (PI, rainy season between September and October 2019). The presence and number of indoor Aedes females and blood-fed females (indoor mosquito infestation) as well as arboviral infections with dengue (DENV) and Zika (ZIKV) viruses were evaluated in a subsample of 30 houses within each cluster.

RESULTS

HS houses had significantly lower risk for having Aedes aegypti female mosquitoes (odds ratio [OR] = 0.56, 95% CI 0.33-0.97, p = 0.04) and blood-fed females (OR = 0.53, 95% CI 0.28-0.97, p = 0.04) than unscreened households from the control arm. Compared to control houses, HS houses had significantly lower indoor Ae. aegypti abundance (rate ratio [RR] = 0.50, 95% CI 0.30-0.83, p = 0.01), blood-fed Ae. aegypti females (RR = 0.48, 95% CI 0.27-0.85, p = 0.01) and female Ae. aegypti positive for arboviruses (OR = 0.29, 95% CI 0.10-0.86, p = 0.02). The estimated intervention efficacy in reducing Ae. aegypti arbovirus infection was 71%.

CONCLUSIONS

These results provide evidence supporting the use of HS as an effective pesticide-free method to control house infestations with Aedes aegypti and reduce the transmission of Aedes-transmitted viruses such as DENV, chikungunya (CHIKV) and ZIKV.

Interfacing vector-borne disease dynamics with climate change: Implications for the attainment of SDGs in Masvingo city, Zimbabwe

This study used a mixed-methods research design to examine the sensitivity of vector-borne disease (VBD) patterns to the changes in rainfall and temperature trends. The research focused on malaria in Masvingo Province, Zimbabwe. The study interfaced the climate action, health and sustainable cities and communities with sustainable development goals (SDGs). Historical climate and epidemiological data were used to compute the correlations and determine the possible modifications of disease patterns. Clustered random and chain-referral sampling approaches were used to select study sites and respondents. Primary data were gathered through a questionnaire survey (n = 191), interviews and focus group discussions, with Mann-Kendal trend tests performed using XLSTAT 2020. The results show a positive correlation between malaria prevalence rates and temperature-related variables. A decline in precipitation-related variables, specifically mean monthly precipitation (MMP), was associated with an increase in malaria prevalence. These observations were confirmed by the views of the respondents, which show that climate change has a bearing on malaria spatial and temporal dynamics in Masvingo Province. The study concludes that climate change plays a contributory role in VBD dynamics, thereby impeding the attainment of the 2030 Agenda for Sustainable Development, especially SDG 3, which deals with health. The study recommends further research into appropriate adaptation mechanisms to increase the resilience of rural and urban communities against the negative transmutations associated with weather and climatic pressures.

Comparing machine learning with case-control models to identify confirmed dengue cases

In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have applied newly developed machine learning approaches to identify laboratory-confirmed dengue cases from 4,894 emergency department patients with dengue-like illness (DLI) who received laboratory tests. Among them, 60.11% (2942 cases) were confirmed to have dengue. Using just four input variables [age, body temperature, white blood cells counts (WBCs) and platelets], not only the state-of-the-art deep neural network (DNN) prediction models but also the conventional decision tree (DT) and logistic regression (LR) models delivered performances with receiver operating characteristic (ROC) curves areas under curves (AUCs) of the ranging from 83.75% to 85.87% [for DT, DNN and LR: 84.60% ± 0.03%, 85.87% ± 0.54%, 83.75% ± 0.17%, respectively]. Subgroup analyses found all the models were very sensitive particularly in the pre-epidemic period. Pre-peak sensitivities (<35 weeks) were 92.6%, 92.9%, and 93.1% in DT, DNN, and LR respectively. Adjusted odds ratios examined with LR for low WBCs [≤ 3.2 (x103/μL)], fever (≥38°C), low platelet counts [< 100 (x103/μL)], and elderly (≥ 65 years) were 5.17 [95% confidence interval (CI): 3.96-6.76], 3.17 [95%CI: 2.74-3.66], 3.10 [95%CI: 2.44-3.94], and 1.77 [95%CI: 1.50-2.10], respectively. Our prediction models can readily be used in resource-poor countries where viral/serologic tests are inconvenient and can also be applied for real-time syndromic surveillance to monitor trends of dengue cases and even be integrated with mosquito/environment surveillance for early warning and immediate prevention/control measures. In other words, a local community hospital/clinic with an instrument of complete blood counts (including platelets) can provide a sentinel screening during outbreaks. In conclusion, the machine learning approach can facilitate medical and public health efforts to minimize the health threat of dengue epidemics. However, laboratory confirmation remains the primary goal of surveillance and outbreak investigation.