Predicting seasonal influenza epidemics using cross-hemisphere influenza surveillance data and local internet query data

Estimating epidemic trajectories of SARS-CoV-2 and influenza A virus based on wastewater monitoring and a novel machine learning algorithm

The COVID-19 pandemic has altered the circulation of non-SARS-CoV-2 respiratory viruses. In this study, we carried out wastewater surveillance of SARS-CoV-2 and influenza A virus (IAV) in three key port cities in China through real-time quantitative PCR (RT-qPCR). Next, a novel machine learning algorithm (MLA) based on Gaussian model and random forest model was used to predict the epidemic trajectories of SARS-CoV-2 and IAV. The results showed that from February 2023 to January 2024, three port cities experienced two waves of SARS-CoV-2 infection, which peaked in late-May and late-August 2023, respectively. Two waves of IAV were observed in the spring and winter of 2023, respectively with considerable variations in terms of onset/offset date and duration. Furthermore, we employed MLA to extract the key features of epidemic trajectories of SARS-CoV-2 and IAV from February 3rd, to October 15th, 2023, and thereby predicted the epidemic trends of SARS-CoV-2 and IAV from October 16th, 2023 to April 22nd, 2024, which showed high consistency with the observed values. These collective findings offer an important understanding of SARS-CoV-2 and IAV epidemics, suggesting that wastewater surveillance together with MLA emerges as a powerful tool for risk assessment of respiratory viral diseases and improving public health preparedness.

Internet-based Surveillance Systems and Infectious Diseases Prediction: An Updated Review of the Last 10 Years and Lessons from the COVID-19 Pandemic

The last decade has seen major advances and growth in internet-based surveillance for infectious diseases through advanced computational capacity, growing adoption of smart devices, increased availability of Artificial Intelligence (AI), alongside environmental pressures including climate and land use change contributing to increased threat and spread of pandemics and emerging infectious diseases. With the increasing burden of infectious diseases and the COVID-19 pandemic, the need for developing novel technologies and integrating internet-based data approaches to improving infectious disease surveillance is greater than ever. In this systematic review, we searched the scientific literature for research on internet-based or digital surveillance for influenza, dengue fever and COVID-19 from 2013 to 2023. We have provided an overview of recent internet-based surveillance research for emerging infectious diseases (EID), describing changes in the digital landscape, with recommendations for future research directed at public health policymakers, healthcare providers, and government health departments to enhance traditional surveillance for detecting, monitoring, reporting, and responding to influenza, dengue, and COVID-19.

Developing a Research Network of Early Warning Systems for Infectious Diseases Transmission Between China and Australia

This article offers a thorough review of current early warning systems (EWS) and advocates for establishing a unified research network for EWS in infectious diseases between China and Australia. We propose that future research should focus on improving infectious disease surveillance by integrating data from both countries to enhance predictive models and intervention strategies. The article highlights the need for standardized data formats and terminologies, improved surveillance capabilities, and the development of robust spatiotemporal predictive models. It concludes by examining the potential benefits and challenges of this collaborative approach and its implications for global infectious disease surveillance. This is particularly relevant to the ongoing project, early warning systems for Infectious Diseases between China and Australia (NetEWAC), which aims to use seasonal influenza as a case study to analyze influenza trends, peak activities, and potential inter-hemispheric transmission patterns. The project seeks to integrate data from both hemispheres to improve outbreak predictions and develop a spatiotemporal predictive modeling system for seasonal influenza transmission based on socio-environmental factors.

Predicting seasonal influenza outbreaks with regime shift-informed dynamics for improved public health preparedness

In this study, we propose a novel approach that integrates regime-shift detection with a mechanistic model to forecast the peak times of seasonal influenza. The key benefit of this approach is its ability to detect regime shifts from non-epidemic to epidemic states, which is particularly beneficial with the year-round presence of non-zero Influenza-Like Illness (ILI) data. This integration allows for the incorporation of external factors that trigger the onset of the influenza season-factors that mechanistic models alone might not adequately capture. Applied to ILI data collected in Korea from 2005 to 2020, our method demonstrated stable peak time predictions for seasonal influenza outbreaks, particularly in years characterized by unusual onset times or epidemic magnitudes.

Does the Australian influenza season predict the Canadian influenza season? A qualitative comparison of seasons, 2014-2020

A commonly held belief by the Canadian media and public is that the Australian influenza season is a fairly reliable indicator of what the Canadian influenza season that follows might be like. However, this claim is not well substantiated with epidemiological evidence. Therefore, the objective of this work was to qualitatively compare the timing of the onset, peak, and intensity of influenza activity, the dominant circulating influenza strains, and the seasonal vaccine and vaccination policies from 2014 to 2020 between Canada and Australia, using a combination of FluNet data and influenza surveillance reports and publications. Across the epidemiological indicators considered, the epidemics between Canada and Australia often differ. While vaccination policies and coverage are similar between the two countries, vaccine composition and vaccine effectiveness estimates also differ. Ultimately, there are many differences and confounding variables between the Australian and Canadian influenza seasons across numerous indicators that preclude the use of the Australian influenza season as the sole predictor of the Canadian influenza season. However, the availability of global surveillance data and robust national and sub-national surveillance data can provide lead time and inform within-season resource and capacity planning, as well as mitigation measures, for seasonal influenza epidemics.

Praemonitus praemunitus: can we forecast and prepare for future viral disease outbreaks?

Understanding the origins of past and present viral epidemics is critical in preparing for future outbreaks. Many viruses, including SARS-CoV-2, have led to significant consequences not only due to their virulence, but also because we were unprepared for their emergence. We need to learn from large amounts of data accumulated from well-studied, past pandemics and employ modern informatics and therapeutic development technologies to forecast future pandemics and help minimize their potential impacts. While acknowledging the complexity and difficulties associated with establishing reliable outbreak predictions, herein we provide a perspective on the regions of the world that are most likely to be impacted by future outbreaks. We specifically focus on viruses with epidemic potential, namely SARS-CoV-2, MERS-CoV, DENV, ZIKV, MAYV, LASV, noroviruses, influenza, Nipah virus, hantaviruses, Oropouche virus, MARV, and Ebola virus, which all require attention from both the public and scientific community to avoid societal catastrophes like COVID-19. Based on our literature review, data analysis, and outbreak simulations, we posit that these future viral epidemics are unavoidable, but that their societal impacts can be minimized by strategic investment into basic virology research, epidemiological studies of neglected viral diseases, and antiviral drug discovery.

Using the Baidu index to predict trends in the incidence of tuberculosis in Jiangsu Province, China

Objective

To analyze the time series in the correlation between search terms related to tuberculosis (TB) and actual incidence data in China. To screen out the "leading" terms and construct a timely and efficient TB prediction model that can predict the next wave of TB epidemic trend in advance.

Methods

Monthly incidence data of tuberculosis in Jiangsu Province, China, were collected from January 2011 to December 2020. A scoping approach was used to identify TB search terms around common TB terms, prevention, symptoms and treatment. Search terms for Jiangsu Province, China, from January 2011 to December 2020 were collected from the Baidu index database. Correlation coefficients between search terms and actual incidence were calculated using Python 3.6 software. The multiple linear regression model was constructed using SPSS 26.0 software, which also calculated the goodness of fit and prediction error of the model predictions.

Results

A total of 16 keywords with correlation coefficients greater than 0.6 were screened, of which 11 were the leading terms. The R2 of the prediction model was 0.67 and the MAPE was 10.23%.

Conclusion

The TB prediction model based on Baidu Index data was able to predict the next wave of TB epidemic trends and intensity 2 months in advance. This forecasting model is currently only available for Jiangsu Province.

Preparing for the Next Influenza Season: Monitoring the Emergence and Spread of Antiviral Resistance

Purpose

The relaxation of pandemic restrictions in 2022 has led to a reemergence of respiratory virus circulation worldwide and anticipation of substantial influenza waves for the 2022/2023 Northern Hemisphere winter. Therefore, the antiviral susceptibility profiles of human influenza viruses circulating in Germany were characterized.

Methods

Between October 2019 (week 40/2019) and March 2022 (week 12/2022), nasal swabs from untreated patients with acute respiratory symptoms were collected in the national German influenza surveillance system. A total of 598 influenza viruses were isolated and analyzed for susceptibility to oseltamivir, zanamivir and peramivir, using a neuraminidase (NA) inhibition assay. In addition, next-generation sequencing was applied to assess molecular markers of resistance to NA, cap-dependent endonuclease (PA) and M2 ion channel inhibitors (NAI, PAI, M2I) in 367 primary clinical samples. Furthermore, a genotyping assay based on RT-PCR and pyrosequencing to rapidly assess the molecular resistance marker PA-I38X in PA genes was designed and established.

Results

While NAI resistance in the strict sense, defined by a ≥ 10-fold (influenza A) or ≥5-fold (influenza B) increase of NAI IC₅₀, was not detected, a subtype A(H1N1)pdm09 isolate displayed 2.3- to 7.5-fold IC₅₀ increase for all three NAI. This isolate carried the NA-S247N substitution, which is known to enhance NAI resistance induced by NA-H275Y. All sequenced influenza A viruses carried the M2-S31N substitution, which confers resistance to M2I. Of note, one A(H3N2) virus displayed the PA-I38M substitution, which is associated with reduced susceptibility to the PAI baloxavir marboxil. Pyrosequencing analysis confirmed these findings in the original clinical specimen and in cultured virus isolate, suggesting sufficient replicative fitness of this virus mutant.

Conclusion

Over the last three influenza seasons, the vast majority of influenza viruses in this national-level sentinel were susceptible to NAIs and PAIs. These findings support the use of antivirals in the upcoming influenza season.

Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Algorithm Development and Validation

BACKGROUND

Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks. Most prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone (O₃), oxides of nitrogen, and fine particulate matter (PM_2.5). Given that traditional, highly sophisticated air quality monitors are expensive and not universally available, these models cannot adequately serve those not living near pollutant monitoring sites. Furthermore, because prior models were built based on physical measurement data collected from sensors, they may not be suitable for predicting the public health effects of pollution exposure.

OBJECTIVE

This study aimed to develop and validate models to nowcast the observed pollution levels using web search data, which are publicly available in near real time from major search engines.

METHODS

We developed novel machine learning-based models using both traditional supervised classification methods and state-of-the-art deep learning methods to detect elevated air pollution levels at the US city level by using generally available meteorological data and aggregate web-based search volume data derived from Google Trends. We validated the performance of these methods by predicting 3 critical air pollutants (O₃, nitrogen dioxide, and PM_2.5) across 10 major US metropolitan statistical areas in 2017 and 2018. We also explore different variations of the long short-term memory model and propose a novel search term dictionary learner-long short-term memory model to learn sequential patterns across multiple search terms for prediction.

RESULTS

The top-performing model was a deep neural sequence model long short-term memory, using meteorological and web search data, and reached an accuracy of 0.82 (F₁-score 0.51) for O_3, 0.74 (F₁-score 0.41) for nitrogen dioxide, and 0.85 (F₁-score 0.27) for PM_2.5, when used for detecting elevated pollution levels. Compared with using only meteorological data, the proposed method achieved superior accuracy by incorporating web search data.

CONCLUSIONS

The results show that incorporating web search data with meteorological data improves the nowcasting performance for all 3 pollutants and suggest promising novel applications for tracking global physical phenomena using web search data.

A potential tool for predicting epidemic trends and outbreaks of scrub typhus based on Internet search big data analysis in Yunnan Province, China

Introduction

Scrub typhus, caused by Orientia tsutsugamushi, is a neglected tropical disease. The southern part of China is considered an important epidemic and conserved area of scrub typhus. Although a surveillance system has been established, the surveillance of scrub typhus is typically delayed or incomplete and cannot predict trends in morbidity. Internet search data intuitively expose the public's attention to certain diseases when used in the public health area, thus reflecting the prevalence of the diseases.

Methods

In this study, based on the Internet search big data and historical scrub typhus incidence data in Yunnan Province of China, the autoregressive integrated moving average (ARIMA) model and ARIMA with external variables (ARIMAX) model were constructed and compared to predict the scrub typhus incidence.

Results

The results showed that the ARIMAX model produced a better outcome than the ARIMA model evaluated by various indexes and comparisons with the actual data.

Conclusions

The study demonstrates that Internet search big data can enhance the traditional surveillance system in monitoring and predicting the prevalence of scrub typhus and provides a potential tool for monitoring epidemic trends of scrub typhus and early warning of its outbreaks.

Developing spatio-temporal approach to predict economic dynamics based on online news

Economic forecasting is a scientific decision-making tool, and it is one of the important basis for the government to formulate economic plans, predict the implementation of the plan, and guide the implementation of the plan. Current knowledge about the use of online news in the prediction of economic patterns in China is limited, especially considering the spatio-temporal dynamics over time. This study explored the spatio-temporal patterns of economic output values in Yinzhou, Ningbo, China between 2018 and 2021, and proposed generalized linear model (GLM) and Geographically weighted regression (GWR) model to predict the dynamics using online news data. The results indicated that there were spatio-temporal variations in the economic dynamics in the study area. The online news showed a great potential to predict economic dynamics, with better performance in the GWR model. The findings suggested online news combining with spatio-temporal approach can better forecast economic dynamics, which can be seen as a pre-requisite for developing an online news-based surveillance system The advanced spatio-temporal analysis enables governments to garner insights about the patterns of economic dynamics over time, which may enhance the ability of government to formulate economic plans and to predict the implementation of the plan. The proposed model may be extended to greater geographic area to validate such approach.

Economics of attention: The gender-based bing communication study on depression

This study examines the impact of personalized gender-based communication to encourage the screening of depression and seeking out mental health care consultation. An internet search engine advertisement was deployed on Bing, Microsoft during the COVID-19 pandemic lockdowns in the Provence–Alpes–Côte d'Azur (PACA) region in France during the month of May 2020, the height of the France lockdowns. A two-armed study was conducted with Arm A containing a non-personalized (control) advertisement and Arm B containing a personalized gender-based advertisement. 53,185 advertisements were shown between the two arms. Results show that receiving a personalized gender-based message increases the probability of clicking on the advertisement. However, upon clicking the advertisement, there was no significant difference in the completion of the depression questionnaire between the two groups. These results suggest that although personalized gender messaging is effective at drawing in a greater click rate, it did not increase, nor decreased, the conversion rate to monitor depression by self-assessment.

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Personalized gender-based messages increase the click-rate probability on an advertisement for self-screening of depression.

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Personalized gender-based messages did not increase/decrease the conversion rate to intend to seek medical help for depression.

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Youth under the age of 25 who were considered depressed were less likely to have the intent to seek future treatment.

A novel data-driven methodology for influenza outbreak detection and prediction

Influenza is an infectious disease that leads to an estimated 5 million cases of severe illness and 650,000 respiratory deaths worldwide each year. The early detection and prediction of influenza outbreaks are crucial for efficient resource planning to save patient’s lives and healthcare costs. We propose a new data-driven methodology for influenza outbreak detection and prediction at very local levels. A doctor’s diagnostic dataset of influenza-like illness from more than 3000 clinics in Malaysia is used in this study because these diagnostic data are reliable and can be captured promptly. A new region index (RI) of the influenza outbreak is proposed based on the diagnostic dataset. By analysing the anomalies in the weekly RI value, potential outbreaks are identified using statistical methods. An ensemble learning method is developed to predict potential influenza outbreaks. Cross-validation is conducted to optimize the hyperparameters of the ensemble model. A testing data set is used to provide an unbiased evaluation of the model. The proposed methodology is shown to be sensitive and accurate at influenza outbreak prediction, with average of 75% recall, 74% precision, and 83% accuracy scores across five regions in Malaysia. The results are also validated by Google Flu Trends data, news reports, and surveillance data released by World Health Organization.

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.

Cardiovascular-related health behaviors and lifestyle during the COVID-19 pandemic: An infodemiology study

Background

Reports have suggested decreasing attention to CVD comorbidities during the COVID-19 pandemic, despite their association with worse virus-related outcomes. Using nowcasting tools, we sought to explore temporal trends in public interest by studying use of online search terms related to CVD comorbidities during the pandemic time period.

Methods

We queried Google Trends for recent (October 2019–October 2020) and seasonal (October 2016–October 2020) trends of search terms pertaining to cardiovascular-related behaviors or clinical care including clinical diagnostic and therapeutic-related terms. Additionally, we acquired data from Google Shopping Insights to explore consumer behavior. Data for search results in the US were compared using mean relative search volumes (RSV), tabulated by month.

Results

Online search interest in the terms “Exercise” and “Cigarettes” changed by +18.0% and −52.5%, respectively, comparing March–April with January–February 2020. Key terms related to CVD-related care, including diagnostic and therapeutic-related terms such as “Statin”, “Lipid profile”, “Low-density lipoprotein”, and “Hemoglobin A1C” declined to a four-year low in late March 2020 but regained pre-pandemic search query frequency by July 2020. Results were supported by Google Shopping analysis; for example, online consumer shopping-related searches for tobacco products reached at an all-year low after May 2020.

Conclusion

We report an increase in search interest for an overall healthier CVD-related lifestyle starting in March 2020, supported by online consumer shopping behavior. However, a months-long trough in public interest for CVD care-related search terms from March–May 2020 suggests a transient but substantial decrease in public focus on cardiovascular-related healthcare engagement. Future research is needed to understand if these mixed signals will persist into 2021 and how they potentially translate into real-world CVD-related event rates.

Survey data and human computation for improved flu tracking

While digital trace data from sources like search engines hold enormous potential for tracking and understanding human behavior, these streams of data lack information about the actual experiences of those individuals generating the data. Moreover, most current methods ignore or under-utilize human processing capabilities that allow humans to solve problems not yet solvable by computers (human computation). We demonstrate how behavioral research, linking digital and real-world behavior, along with human computation, can be utilized to improve the performance of studies using digital data streams. This study looks at the use of search data to track prevalence of Influenza-Like Illness (ILI). We build a behavioral model of flu search based on survey data linked to users' online browsing data. We then utilize human computation for classifying search strings. Leveraging these resources, we construct a tracking model of ILI prevalence that outperforms strong historical benchmarks using only a limited stream of search data and lends itself to tracking ILI in smaller geographic units. While this paper only addresses searches related to ILI, the method we describe has potential for tracking a broad set of phenomena in near real-time.

Weather Variability and COVID-19 Transmission: A Review of Recent Research

Weather and climate play a significant role in infectious disease transmission, through changes to transmission dynamics, host susceptibility and virus survival in the environment. Exploring the association of weather variables and COVID-19 transmission is vital in understanding the potential for seasonality and future outbreaks and developing early warning systems. Previous research examined the effects of weather on COVID-19, but the findings appeared inconsistent. This review aims to summarize the currently available literature on the association between weather and COVID-19 incidence and provide possible suggestions for developing weather-based early warning system for COVID-19 transmission. Studies eligible for inclusion used ecological methods to evaluate associations between weather (i.e., temperature, humidity, wind speed and rainfall) and COVID-19 transmission. The review showed that temperature was reported as significant in the greatest number of studies, with COVID-19 incidence increasing as temperature decreased and the highest incidence reported in the temperature range of 0–17 °C. Humidity was also significantly associated with COVID-19 incidence, though the reported results were mixed, with studies reporting positive and negative correlation. A significant interaction between humidity and temperature was also reported. Wind speed and rainfall results were not consistent across studies. Weather variables including temperature and humidity can contribute to increased transmission of COVID-19, particularly in winter conditions through increased host susceptibility and viability of the virus. While there is less indication of an association with wind speed and rainfall, these may contribute to behavioral changes that decrease exposure and risk of infection. Understanding the implications of associations with weather variables and seasonal variations for monitoring and control of future outbreaks is essential for early warning systems.

Big data analytics as a tool for fighting pandemics: a systematic review of literature

Infectious and contagious diseases represent a major challenge for health systems worldwide, either in private or public sectors. More recently, with the increase in cases related to these problems, combined with the recent global pandemic of COVID-19, the need to study strategies to treat these health disturbs is even more latent. Big Data, as well as Big Data Analytics techniques, have been addressed in this context with the possibility of predicting, mapping, tracking, monitoring, and raising awareness about these epidemics and pandemics. Thus, the purpose of this study is to identify how BDA can help in cases of pandemics and epidemics. To achieve this purpose, a systematic review of literature was carried out using the methodology Methodi Ordinatio. The rigorous search resulted in a portfolio of 45 articles, retrived from scientific databases. For the collection and analysis of data, the softwares NVivo 12 and VOSviewer were used. The content analysis sought to identify how Big Data and Big Data Analytics can help fighting epidemics and pandemics. The types and sources of data used in cases of previous epidemics and pandemics were identified, as well as techniques for treating these data. The results showed that the main sources of data come from social media and Internet search engines. The most common techniques for analyzing these data involve the use of statistics, such as correlation and regression, combined with other techniques. Results shows that there is a fruitiful field of study to be explored by both areas, Big Data and Health.

Forecasting COVID-19 in Pakistan

OBJECTIVES

Forecasting epidemics like COVID-19 is of crucial importance, it will not only help the governments but also, the medical practitioners to know the future trajectory of the spread, which might help them with the best possible treatments, precautionary measures and protections. In this study, the popular autoregressive integrated moving average (ARIMA) will be used to forecast the cumulative number of confirmed, recovered cases, and the number of deaths in Pakistan from COVID-19 spanning June 25, 2020 to July 04, 2020 (10 days ahead forecast).

METHODS

To meet the desire objectives, data for this study have been taken from the Ministry of National Health Service of Pakistan's website from February 27, 2020 to June 24, 2020. Two different ARIMA models will be used to obtain the next 10 days ahead point and 95% interval forecast of the cumulative confirmed cases, recovered cases, and deaths. Statistical software, RStudio, with "forecast", "ggplot2", "tseries", and "seasonal" packages have been used for data analysis.

RESULTS

The forecasted cumulative confirmed cases, recovered, and the number of deaths up to July 04, 2020 are 231239 with a 95% prediction interval of (219648, 242832), 111616 with a prediction interval of (101063, 122168), and 5043 with a 95% prediction interval of (4791, 5295) respectively. Statistical measures i.e. root mean square error (RMSE) and mean absolute error (MAE) are used for model accuracy. It is evident from the analysis results that the ARIMA and seasonal ARIMA model is better than the other time series models in terms of forecasting accuracy and hence recommended to be used for forecasting epidemics like COVID-19.

CONCLUSION

It is concluded from this study that the forecasting accuracy of ARIMA models in terms of RMSE, and MAE are better than the other time series models, and therefore could be considered a good forecasting tool in forecasting the spread, recoveries, and deaths from the current outbreak of COVID-19. Besides, this study can also help the decision-makers in developing short-term strategies with regards to the current number of disease occurrences until an appropriate medication is developed.

Declining interest in clinical imaging during the COVID-19 pandemic: An analysis of Google Trends data

OBJECTIVE

Current evidence suggests a decrease in elective diagnostic imaging procedures during the COVID-19 pandemic with potentially severe long-term consequences. The aim of this study was to quantify recent trends in public interest and related online search behavior for a range of imaging modalities, and "nowcast" future scenarios with respect to imaging use.

METHODS

We used Google Trends, a publicly available database to access search query data in systematic and quantitative fashion, to search for key terms related to clinical imaging. We queried the search volume for multiple imaging modalities, identified the most common terms, extracted data for the United States over the time range from August 1, 2016 to August 1, 2020. Results were given in relative terms, using the Google metric 'search volume index'.

RESULTS

We report a decrease in public interest across all imaging modalities since March 2020 with a subsequent slow increase starting in May 2020. Mean relative search volume (RSV) has changed by -19.4%, -38.3%, and -51.0% for the search terms "Computed tomography", "Magnetic resonance imaging", and "Mammography", respectively, and comparing the two months prior to and following March 1, 2020. RSV has since steadily recuperated reaching all-year highs.

CONCLUSION

Decrease in public interest coupled with delays and deferrals of diagnostic imaging will likely result in a high demand for healthcare in the coming months. To respond to this challenge, measures such as risk-stratification algorithms must be developed to allocate resources and avoid the risk of overstraining the healthcare system.

Predictive modeling of COVID-19 death cases in Pakistan

BACKGROUND

The world is presently facing the challenges posed by COVID-19 (2019-nCoV), especially in the public health sector, and these challenges are dangerous to both health and life. The disease results in an acute respiratory infection that may result in pain and death. In Pakistan, the disease curve shows a vertical trend by almost 256K established cases of the diseases and 6035 documented death cases till August 5, 2020.

OBJECTIVE

The primary purpose of this study is to provide the statistical model to predict the trend of COVID-19 death cases in Pakistan. The age and gender of COVID-19 victims were represented using a descriptive study.

METHOD

ology: Three regression models, which include Linear, logarithmic, and quadratic, were employed in this study for the modelling of COVID-19 death cases in Pakistan. These three models were compared based on R2, Adjusted R2, AIC, and BIC criterions. The data utilized for the modelling was obtained from the National Institute of Health of Pakistan from February 26, 2020 to August 5, 2020.

CONCLUSION

The finding deduced after the prediction modelling is that the rate of mortality would decrease by the end of October. The total number of deaths will reach its maximum point; then, it will gradually decrease. This indicates that the curve of total deaths will continue to be flat, i.e., it will shift to be constant, which is also the upper bound of the underlying function of absolute death.

Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America

Infodemiology is the process of mining unstructured and textual data so as to provide public health officials and policymakers with valuable information regarding public health. The appearance of this new data source, which was previously unimaginable, has opened up a new way in which to improve public health systems, resulting in better communication policies and better detection systems. However, the unstructured nature of the Internet, along with the complexity of the infectious disease domain, prevents the information extracted from being easily understood. Moreover, when dealing with languages other than English, for which some of the most common Natural Language Processing resources are not available, the correct exploitation of this data becomes even more difficult. We intend to fill these gaps proposing an ontology-driven aspect-based sentiment analysis with which to measure the general public's opinions as regards infectious diseases when expressed in Spanish by employing a case study of tweets concerning the Zika, Dengue and Chikungunya viruses in Latin America. Our proposal is based on two technologies. We first use ontologies in order to model the infectious disease domain with concepts such as risks, symptoms, transmission methods or drugs, among other concepts. We then measure the relationship between these concepts in order to determine the degree to which one concept influences other concepts. This new information is subsequently applied in order to build an aspect-based sentiment analysis model based on statistical and linguistic features. This is done by applying deep-learning models. Our proposal is available on a web platform, where users can see the sentiment for each concept at a glance and analyse how each concept influences the sentiment of the others.

Surveillance of early stage COVID-19 clusters using search query logs and mobile device-based location information

Two clusters of the coronavirus disease 2019 (COVID-19) were confirmed in Hokkaido, Japan, in February 2020. To identify these clusters, this study employed web search query logs of multiple devices and user location information from location-aware mobile devices. We anonymously identified users who used a web search engine (i.e., Yahoo! JAPAN) to search for COVID-19 or its symptoms. We regarded them as web searchers who were suspicious of their own COVID-19 infection (WSSCI). We extracted the location of WSSCI via a mobile operating system application and compared the spatio-temporal distribution of WSSCI with the actual location of the two known clusters. In the early stage of cluster development, we confirmed several WSSCI. Our approach was accurate in this stage and became biased after a public announcement of the cluster development. When other cluster-related resources, such as detailed population statistics, are not available, the proposed metric can capture hints of emerging clusters.

Antigenic Variability

Protective vaccines for hypervariable pathogens are urgently needed. It has been proposed that amputating highly variable epitopes from vaccine antigens would induce the production of broadly protective antibodies targeting conserved epitopes. However, so far, these approaches have failed, partially because conserved epitopes are occluded in vivo and partially because co-localizing patterns of immunodominance and antigenic variability render variable epitopes the primary target for antibodies in natural infection. In this Perspective, to recast the challenge of vaccine development for hypervariable pathogens, I evaluate convergent mechanisms of adaptive variation, such as intrahost immune-mediated diversification, spatiotemporally defined antigenic space, and infection-enhancing cross-immunoreactivity. The requirements of broadly protective immune responses targeting variable pathogens are formulated in terms of cross-immunoreactivity, stoichiometric thresholds for neutralization, and the elicitation of antibodies targeting physicochemically conserved signatures within sequence variable domains.

Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study

Background

South Korea is among the best-performing countries in tackling the coronavirus pandemic by using mass drive-through testing, face mask use, and extensive social distancing. However, understanding the patterns of risk perception could also facilitate effective risk communication to minimize the impacts of disease spread during this crisis.

Objective

We attempt to explore patterns of community health risk perceptions of COVID-19 in South Korea using internet search data.

Methods

Google Trends (GT) and NAVER relative search volumes (RSVs) data were collected using COVID-19–related terms in the Korean language and were retrieved according to time, gender, age groups, types of device, and location. Online queries were compared to the number of daily new COVID-19 cases and tests reported in the Kaggle open-access data set for the time period of December 5, 2019, to May 31, 2020. Time-lag correlations calculated by Spearman rank correlation coefficients were employed to assess whether correlations between new COVID-19 cases and internet searches were affected by time. We also constructed a prediction model of new COVID-19 cases using the number of COVID-19 cases, tests, and GT and NAVER RSVs in lag periods (of 1-3 days). Single and multiple regressions were employed using backward elimination and a variance inflation factor of <5.

Results

The numbers of COVID-19–related queries in South Korea increased during local events including local transmission, approval of coronavirus test kits, implementation of coronavirus drive-through tests, a face mask shortage, and a widespread campaign for social distancing as well as during international events such as the announcement of a Public Health Emergency of International Concern by the World Health Organization. Online queries were also stronger in women (r=0.763-0.823; P<.001) and age groups ≤29 years (r=0.726-0.821; P<.001), 30-44 years (r=0.701-0.826; P<.001), and ≥50 years (r=0.706-0.725; P<.001). In terms of spatial distribution, internet search data were higher in affected areas. Moreover, greater correlations were found in mobile searches (r=0.704-0.804; P<.001) compared to those of desktop searches (r=0.705-0.717; P<.001), indicating changing behaviors in searching for online health information during the outbreak. These varied internet searches related to COVID-19 represented community health risk perceptions. In addition, as a country with a high number of coronavirus tests, results showed that adults perceived coronavirus test–related information as being more important than disease-related knowledge. Meanwhile, younger, and older age groups had different perceptions. Moreover, NAVER RSVs can potentially be used for health risk perception assessments and disease predictions. Adding COVID-19–related searches provided by NAVER could increase the performance of the model compared to that of the COVID-19 case–based model and potentially be used to predict epidemic curves.

Conclusions

The use of both GT and NAVER RSVs to explore patterns of community health risk perceptions could be beneficial for targeting risk communication from several perspectives, including time, population characteristics, and location.

COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population

The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow governments to alter their policy accordingly and plan ahead for the preventive steps needed such as public health messaging, raising awareness of citizens and increasing the capacity of the health system. This study investigated the accuracy of a variety of time series modeling approaches for coronavirus outbreak detection in ten different countries with the highest number of confirmed cases as of 4 May 2020. For each of these countries, six different time series approaches were developed and compared using two publicly available datasets regarding the progression of the virus in each country and the population of each country, respectively. The results demonstrate that, given data produced using actual testing for a small portion of the population, machine learning time series methods can learn and scale to accurately estimate the percentage of the total population that will become affected in the future.

Avian Influenza A (H7N9) and related Internet search query data in China

The use of Internet-based systems for infectious disease surveillance has been increasingly explored in recent years. However, few studies have used Internet search query or social media data to monitor spatial and temporal trends of avian influenza in China. This study investigated the potential of using search query and social media data in detecting and monitoring avian influenza A (H7N9) cases in humans in China. We collected weekly data on laboratory-confirmed H7N9 cases in humans, as well as H7N9-related Baidu Search Index (BSI) and Weibo Posting Index (WPI) data in China from 2013 to 2017, to explore the spatial and temporal trends of H7N9 cases and H7N9-related Internet search queries. Our findings showed a positive relationship of H7N9 cases with BSI and WPI search queries spatially and temporally. The outbreak threshold time and peak time of H7N9-related BSI and WPI searches preceded H7N9 cases in most years. Seasonal autoregressive integrated moving average (SARIMA) models with BSI (β = 0.008, p < 0.001) and WPI (β = 0.002, p = 0.036) were used to predict the number of H7N9 cases. Regression tree model analysis showed that the average H7N9 cases increased by over 2.4-fold (26.8/11) when BSI for H7N9 was >  = 11524. Both BSI and WPI data could be used as indicators to develop an early warning system for H7N9 outbreaks in the future.