Risk assessment strategies for early detection and prediction of infectious disease outbreaks associated with climate change

Effect of a Web-Based Nutritional and Physical Activity Intervention With Email Support (the EDDY Program) on Primary School Children's BMI Z-Score During the COVID-19 Pandemic: Intervention Study

Background

COVID-19 mitigation measures enhanced increases in children's weight and BMI due to decreased physical activity and increased energy intake. Overweight and obesity were major worldwide problems before the pandemic, and COVID-19 increased their severity even more. High BMI directly correlates with health disadvantages including cardiovascular diseases, musculoskeletal disorders, and mental health diseases. Therefore, it is vitally important to develop counteracting interventions to maintain children's health during exceptional situations like pandemics. However, worldwide data from such interventions are limited, and to our knowledge, no suitable study has been carried out during the pandemic in Austria.

Objective

This study was conducted to examine a 15-week web-based intervention with email support, the EDDY (Effect of Sports and Diet Trainings to Prevent Obesity and Secondary Diseases and to Influence Young Children's Lifestyle) program and the effect of nutritional education and physical activity on children's BMI z-score during the COVID-19 pandemic in Vienna, Austria.

Methods

The intervention consisted of 3 weekly videos-2 physical activity and 1 nutritional education video, respectively-and a biweekly email newsletter for the parents. This study was conducted in a Viennese primary school from February to June 2021 by a team of physicians, nutritionists, and sports scientists, including both professionals and students. The study population included an intervention group (who received web-based nutritional and physical activity training) and a control group (who received no intervention), comprising in total 125 children aged 8 to 11 years. Due to COVID-19 mitigation measures, the control group was a comparative group observed during the prior school year (2019-2020). Anthropometric measurements were obtained before and after the intervention in both groups.

Results

Due to a high dropout rate (n=57, 45.6%) because of the mitigation measures, there were 41 children in the intervention group and 27 in the control group. At baseline, the BMI z-score was 1.0 (SD 1.1) in the intervention group and 0.6 (SD 1.2) in the control group (P=.17). After the study period, the BMI z-score decreased by 0.06 (SD 0.21) in the intervention group, whereas it increased by 0.17 (SD 0.34) in the control group (P<.001). Comparing the change in BMI z-scores within BMI categories in the intervention group and control group revealed a statistically significant difference in the normal-weight children (P=.006). Further results showed that the decrease in BMI z-score was significant in the intervention group among both boys (P=.004) and girls (P=.01).

Conclusions

A web-based intervention with combined nutritional education and physical activity training might be an adequate tool to lessen the enhanced increase in body weight during a pandemic. Therefore, additional studies with greater sample sizes and different locations are needed. As the implementation of such intervention programs is essential, further studies need to be established rapidly.

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

Deep Similarity Analysis and Forecasting of Actual Outbreak of Major Infectious Diseases using Internet-Sourced Data

Perhaps no other generation in the span of recorded human history has endured the risks of infectious diseases as has the current generation. The prevalence of infectious diseases is caused mainly by unlimited contact between people in a highly globalized world. Disease control and prevention (CDC) promptly collect and produce disease outbreak statistics, but CDCs rely on a curated, centralized collection system, and requires up to two weeks of lead time. Consequently, the quick release of disease outbreak information has become a great challenge. Infectious disease outbreak information is recorded and spread somewhere on the Internet much faster than CDC announcements, and Internet-sourced data have shown non-substitutable potential to watch and predict infectious disease outbreaks in advance. In this study, we performed a thorough analysis to show the similarity between the Korean Center of Disease Control (KCDC) infectious disease datasets and three Internet-sourced data for nine major infectious diseases in terms of time-series volume. The results show that many of infectious disease outbreak have strongly related to Internet-sourced data. We analyzed several factors that affect the similarity. Our analysis shows that the increase in the number of Internet-sourced data correlates with the increase in the number of infected people and thus, show the positive similarity. We also found that the greater the number of infectious disease outbreaks corresponds to having a wider spread of outbreak regions, in which it also proves to have higher similarity. We presented the prediction result of infectious disease outbreak using various Internet-sourced data and an effective deep learning algorithm. It showed that there are positive correlations between the number of infected people or the number of related web data and the prediction accuracy. We developed and currently operate a web-based system to show the similarity between KCDC and related Internet-sourced data for infectious diseases. This paper helps people to identify what kind of Internet-sourced data they need to use to predict and track a specific infectious disease outbreak. We considered as much as nine major diseases and three kinds of Internet-sourced data together, and we can say that our finding did not depend on specific infectious disease nor specific Internet-sourced data.

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.

Enhancing Influenza Epidemics Forecasting Accuracy in China with Both Official and Unofficial Online News Articles, 2019-2020

Real-time online data sources have contributed to timely and accurate forecasting of influenza activities while also suffered from instability and linguistic noise. Few previous studies have focused on unofficial online news articles, which are abundant in their numbers, rich in information, and relatively low in noise. This study examined whether monitoring both official and unofficial online news articles can improve influenza activity forecasting accuracy during influenza outbreaks. Data were retrieved from a Chinese commercial online platform and the website of the Chinese National Influenza Center. We modeled weekly fractions of influenza-related online news articles and compared them against weekly influenza-like illness (ILI) rates using autoregression analyses. We retrieved 153,958,695 and 149,822,871 online news articles focusing on the south and north of mainland China separately from 6 October 2019 to 17 May 2020. Our model based on online news articles could significantly improve the forecasting accuracy, compared to other influenza surveillance models based on historical ILI rates (p = 0.002 in the south; p = 0.000 in the north) or adding microblog data as an exogenous input (p = 0.029 in the south; p = 0.000 in the north). Our finding also showed that influenza forecasting based on online news articles could be 1-2 weeks ahead of official ILI surveillance reports. The results revealed that monitoring online news articles could supplement traditional influenza surveillance systems, improve resource allocation, and offer models for surveillance of other emerging diseases.

Effect of ambient temperatures on category C notifiable infectious diarrhea in China: An analysis of national surveillance data

BACKGROUND

Many studies have explored the association between meteorological factors and infectious diarrhea (ID) transmission but with inconsistent results, in particular the roles from temperatures. We aimed to explore the effects of temperatures on the transmission of category C ID, to identify its potential heterogeneity in different climate zones of China, and to provide scientific evidence to health authorities and local communities for necessary public health actions.

METHODS

Daily category C ID counts and meteorological variables were collected from 270 cities in China over the period of 2014-16. Distributed lag non-linear models (DLNMs) were applied in each city to obtain the city-specific temperature-disease associations, then a multivariate meta-analysis was implemented to pool the city-specific effects. Multivariate meta-regression was conducted to explore the potential effect modifiers. Attributable fraction was calculated for both low and high temperatures, defined as temperatures below the 5th percentile of temperature or above the 95th percentile of temperature.

RESULTS

A total of 2,715,544 category C ID cases were reported during the study period. Overall, a M-shaped curve relationship was observed between temperature and category C ID, with a peak at the 81st percentile of temperatures (RR = 1.723, 95% CI: 1.579-1.881) compared to 50th percentile of temperatures. The pooled associations were generally stronger at high temperatures compared to low ambient temperatures, and the attributable fraction due to heat was higher than cold. Latitude was identified as a possible effect modifier.

CONCLUSIONS

The overall positive pooled associations between temperature and category C ID in China suggest the increasing temperature could bring about more category C infectious diarrhea cases, which warrants further public health measurements.

Improving National Intelligence for Public Health Preparedness: a methodological approach to finding local multi-sector indicators for health security

The COVID-19 epidemic is the latest evidence of critical gaps in our collective ability to monitor country-level preparedness for health emergencies. The global frameworks that exist to strengthen core public health capacities lack coverage of several preparedness domains and do not provide mechanisms to interface with local intelligence. We designed and piloted a process, in collaboration with three National Public Health Institutes (NPHIs) in Ethiopia, Nigeria and Pakistan, to identify potential preparedness indicators that exist in a myriad of frameworks and tools in varying local institutions. Following a desk-based systematic search and expert consultations, indicators were extracted from existing national and subnational health security-relevant frameworks and prioritised in a multi-stakeholder two-round Delphi process. Eighty-six indicators in Ethiopia, 87 indicators in Nigeria and 51 indicators in Pakistan were assessed to be valid, relevant and feasible. From these, 14–16 indicators were prioritised in each of the three countries for consideration in monitoring and evaluation tools. Priority indicators consistently included private sector metrics, subnational capacities, availability and capacity for electronic surveillance, measures of timeliness for routine reporting, data quality scores and data related to internally displaced persons and returnees. NPHIs play an increasingly central role in health security and must have access to data needed to identify and respond rapidly to public health threats. Collecting and collating local sources of information may prove essential to addressing gaps; it is a necessary step towards improving preparedness and strengthening international health regulations compliance.

Predicting epidemics using search engine data: a comparative study on measles in the largest countries of Europe

BACKGROUND

In recent years new forms of syndromic surveillance that use data from the Internet have been proposed. These have been developed to assist the early prediction of epidemics in various cases and diseases. It has been found that these systems are accurate in monitoring and predicting outbreaks before these are observed in population and, therefore, they can be used as a complement to other methods. In this research, our aim is to examine a highly infectious disease, measles, as there is no extensive literature on forecasting measles using Internet data, METHODS: This research has been conducted with official data on measles for 5 years (2013-2018) from the competent authority of the European Union (European Center of Disease and Prevention - ECDC) and data obtained from Google Trends by using scripts coded in Python. We compared regression models forecasting the development of measles in the five countries.

RESULTS

Results show that measles can be estimated and predicted through Google Trends in terms of time, volume and the overall spread. The combined results reveal a strong relationship of measles cases with the predicted cases (correlation coefficient R= 0.779 in two-tailed significance p< 0.01). The mean standard error was relatively low 45.2 (12.19%) for the combined results. However, major differences and deviations were observed for countries with a relatively low impact of measles, such as the United Kingdom and Spain. For these countries, alternative models were tested in an attempt to improve the results.

CONCLUSIONS

The estimation of measles cases from Google Trends produces acceptable results and can help predict outbreaks in a robust and sound manner, at least 2 months in advance. Python scripts can be used individually or within the framework of an integrated Internet surveillance system for tracking epidemics as the one addressed here.

A forecast model for prevention of foodborne outbreaks of non-typhoidal salmonellosis

Background

This work presents a forecast model for non-typhoidal salmonellosis outbreaks.

Method

This forecast model is based on fitted values of multivariate regression time series that consider diagnosis and estimation of different parameters, through a very flexible statistical treatment called generalized auto-regressive and moving average models (GSARIMA).

Results

The forecast model was validated by analyzing the cases of Salmonella enterica serovar Enteritidis in Sydney Australia (2014–2016), the environmental conditions and the consumption of high-risk food as predictive variables.

Conclusions

The prediction of cases of Salmonella enterica serovar Enteritidis infections are included in a forecast model based on fitted values of time series modeled by GSARIMA, for an early alert of future outbreaks caused by this pathogen, and associated to high-risk food. In this context, the decision makers in the epidemiology field can led to preventive actions using the proposed model.

Using Open-Source Intelligence to Detect Early Signals of COVID-19 in China: Descriptive Study

Background

The coronavirus disease (COVID-19) outbreak in China was first reported to the World Health Organization (WHO) on December 31, 2019, and the first cases were officially identified around December 8, 2019. Although the origin of COVID-19 has not been confirmed, approximately half of the early cases were linked to a seafood market in Wuhan. However, the first two documented patients did not visit the seafood market. News reports, social media, and informal sources may provide information about outbreaks prior to formal notification.

Objective

The aim of this study was to identify early signals of pneumonia or severe acute respiratory illness (SARI) in China prior to official recognition of the COVID-19 outbreak in December 2019 using open-source data.

Methods

To capture early reports, we searched an open source epidemic observatory, EpiWatch, for SARI or pneumonia-related illnesses in China from October 1, 2019. The searches were conducted using Google and the Chinese search engine Baidu.

Results

There was an increase in reports following the official notification of COVID-19 to the WHO on December 31, 2019, and a report that appeared on December 26, 2019 was retracted. A report of severe pneumonia on November 22, 2019, in Xiangyang was identified, and a potential index patient was retrospectively identified on November 17.

Conclusions

The lack of reports of SARI outbreaks prior to December 31, 2019, with a retracted report on December 26, suggests media censorship, given that formal reports indicate that cases began appearing on December 8. However, the findings also support a relatively recent origin of COVID-19 in November 2019. The case reported on November 22 was transferred to Wuhan approximately one incubation period before the first identified cases on December 8; this case should be further investigated, as only half of the early cases were exposed to the seafood market in Wuhan. Another case of COVID-19 has since been retrospectively identified in Hubei on November 17, 2019, suggesting that the infection was present prior to December.

COVID-19 Preparedness Among Emergency Departments: A Cross-Sectional Study in France

Objectives:

The aim of this study was to evaluate hospital and emergency department (ED) preparedness in France facing the coronavirus disease 2019 (COVID-19) rapid growth epidemic-phase, and to determine the link between preparedness and responsiveness.

Methods:

In this cross-sectional study, from March 7 to March 11, 2020, all heads of ED departments in France were contacted to answer an electronic survey, including 23 questions. Quality, Organization, Training, Resources, Management, Interoperability, and Responsiveness were evaluated by calculating scores (10 points). Multivariate analysis of variance was used to compare scores. Spearman’s correlation coefficient and multifaceted regression analysis were performed between Responsiveness and dimensions scores.

Results:

A total of 287 of 636 French EDs were included (45.1%). Calculated scores showed (median): Quality 5.38; Organization 6.4; Training 4.6; Resources 4.13; Management 2.38; Interoperability 4.0; Responsiveness 6.25; seasonal influenza score was 5. Significant differences between scores as a function of hospital and ED main characteristics were found. Furthermore, we found significant correlations (P < 0.01) between Responsiveness and all preparedness dimensions. Organization (adjusted-R² 0.2897), Management (aR² 0.321), and Interoperability (aR² 0.422) were significantly associated with Responsiveness.

Conclusions:

Preparedness in all its dimensions is low, indicating vulnerability. Preparedness and responsiveness face a certain and ongoing risk are close linked, and that Organizational, Management, and Interoperability dimensions are main determinants.

Comparing Social media and Google to detect and predict severe epidemics

Internet technologies have demonstrated their value for the early detection and prediction of epidemics. In diverse cases, electronic surveillance systems can be created by obtaining and analyzing on-line data, complementing other existing monitoring resources. This paper reports the feasibility of building such a system with search engine and social network data. Concretely, this study aims at gathering evidence on which kind of data source leads to better results. Data have been acquired from the Internet by means of a system which gathered real-time data for 23 weeks. Data on influenza in Greece have been collected from Google and Twitter and they have been compared to influenza data from the official authority of Europe. The data were analyzed by using two models: the ARIMA model computed estimations based on weekly sums and a customized approximate model which uses daily sums. Results indicate that influenza was successfully monitored during the test period. Google data show a high Pearson correlation and a relatively low Mean Absolute Percentage Error (R = 0.933, MAPE = 21.358). Twitter results are slightly better (R = 0.943, MAPE = 18.742). The alternative model is slightly worse than the ARIMA(X) (R = 0.863, MAPE = 22.614), but with a higher mean deviation (abs. mean dev: 5.99% vs 4.74%).

The health effects of climate change: Know the risks and become part of the solutions

Climate change presents a clear and present danger to human health. Health impacts are already being demonstrated in Canada, which is warming at roughly twice the global rate. A recent United Nations Environment Emissions Gap Report noted that if countries maintain current emission efforts, emissions will exceed the targets laid out in the Paris Agreement and global warming will exceed 2ºC worldwide. An important consequence of global warming is an increase in health risks. Much can be done to prevent and mitigate the health impacts of climate change, and understanding and communicating these has been shown to be one of the best ways of motivating action. This editorial provides an overview of the some of the global and national initiatives underway to decrease emissions, and address the health risks of climate change in general, and highlights some of the national initiatives underway to mitigate the increased risk of infectious diseases in Canada in particular.