Perceptions and responses to COVID-19 through wastewater surveillance information and online search behavior: A randomized controlled trial (preprint)

Wastewater surveillance is anticipated to be a representative and timely method to assess infectious disease status; however, its influence on public perception and behavior remains unclear. Therefore, in this study, we used a randomized controlled trial to analyze the influence of wastewater surveillance-based information on understanding of, interest in, relief regarding, preventive behavioral intention against, and subsequent online search behavior related to coronavirus disease 2019 (COVID-19). Valid responses were obtained from 1,000 individuals in both control and intervention groups from Yahoo crowdsourcing users aged [≥]18 years in Japan. This survey was conducted from August 4 to August 7, 2023, just before the common Japanese tradition of returning to hometowns. The questionnaire not only collected personal attributes but also gauged responses to COVID-19 information. This information highlighted the early detection capabilities and representativeness of wastewater surveillance compared with sentinel surveillance at medical institutions. At one-week post-survey, we obtained the survey participants' online search history for key words such as "bullet train," "highway," "airplane," and "wastewater." The findings showed no significant differences between the two groups in terms of COVID-19 interest or preventive behavior before information provision, verifying the effectiveness of participant randomization. Wastewater surveillance-based information did not notably elevate understanding or specific intentions regarding COVID-19, such as wearing masks and receiving vaccination. However, it significantly increased interest in, relief concerning the infection status, and general preventive behavioral intentions. Heightened interest and general preventive intentions did not depend on prior interest or behavior. However, those who previously engaged in preventive behavior or who were less interested in COVID-19 exhibited more relief after exposure to wastewater surveillance-based information. Furthermore, this information could slightly influence online searches related to return travel modes, such as highways. In conclusion, information from wastewater surveillance effectively shapes individual perceptions of and responses to infections.

A global systematic analysis of the occurrence, severity, and recovery pattern of long COVID in 2020 and 2021 (preprint)

ImportanceWhile much of the attention on the COVID-19 pandemic was directed at the daily counts of cases and those with serious disease overwhelming health services, increasingly, reports have appeared of people who experience debilitating symptoms after the initial infection. This is popularly known as long COVID. ObjectiveTo estimate by country and territory of the number of patients affected by long COVID in 2020 and 2021, the severity of their symptoms and expected pattern of recovery DesignWe jointly analyzed ten ongoing cohort studies in ten countries for the occurrence of three major symptom clusters of long COVID among representative COVID cases. The defining symptoms of the three clusters (fatigue, cognitive problems, and shortness of breath) are explicitly mentioned in the WHO clinical case definition. For incidence of long COVID, we adopted the minimum duration after infection of three months from the WHO case definition. We pooled data from the contributing studies, two large medical record databases in the United States, and findings from 44 published studies using a Bayesian meta-regression tool. We separately estimated occurrence and pattern of recovery in patients with milder acute infections and those hospitalized. We estimated the incidence and prevalence of long COVID globally and by country in 2020 and 2021 as well as the severity-weighted prevalence using disability weights from the Global Burden of Disease study. ResultsAnalyses are based on detailed information for 1906 community infections and 10526 hospitalized patients from the ten collaborating cohorts, three of which included children. We added published data on 37262 community infections and 9540 hospitalized patients as well as ICD-coded medical record data concerning 1.3 million infections. Globally, in 2020 and 2021, 144.7 million (95% uncertainty interval [UI] 54.8-312.9) people suffered from any of the three symptom clusters of long COVID. This corresponds to 3.69% (1.38-7.96) of all infections. The fatigue, respiratory, and cognitive clusters occurred in 51.0% (16.9-92.4), 60.4% (18.9-89.1), and 35.4% (9.4-75.1) of long COVID cases, respectively. Those with milder acute COVID-19 cases had a quicker estimated recovery (median duration 3.99 months [IQR 3.84-4.20]) than those admitted for the acute infection (median duration 8.84 months [IQR 8.10-9.78]). At twelve months, 15.1% (10.3-21.1) continued to experience long COVID symptoms. Conclusions and relevanceThe occurrence of debilitating ongoing symptoms of COVID-19 is common. Knowing how many people are affected, and for how long, is important to plan for rehabilitative services and support to return to social activities, places of learning, and the workplace when symptoms start to wane. Key PointsO_ST_ABSQuestionC_ST_ABSWhat are the extent and nature of the most common long COVID symptoms by country in 2020 and 2021? FindingsGlobally, 144.7 million people experienced one or more of three symptom clusters (fatigue; cognitive problems; and ongoing respiratory problems) of long COVID three months after infection, in 2020 and 2021. Most cases arose from milder infections. At 12 months after infection, 15.1% of these cases had not yet recovered. MeaningThe substantial number of people with long COVID are in need of rehabilitative care and support to transition back into the workplace or education when symptoms start to wane.

Annual prevalence of non-communicable diseases and identification of vulnerable populations following the Fukushima disaster and COVID-19 pandemic (preprint)

Disasters, pandemics, and their response measures can have secondary effects on the physical and psychological health of affected populations. Identifying populations vulnerable to these effects is beneficial for promoting effective health and prevention strategies. Using health insurance receipt data from 2009 to 2020, we assessed changes in prevalence of major non-communicable diseases (NCDs), including hypertension, hyperlipidemia, diabetes, and mental disorders, among affected populations before and after the Fukushima disaster and coronavirus disease (COVID-19) outbreak in Japan. Furthermore, age and sex groups with the largest increases in prevalence after these events were identified. The participants of this study were members of the Employees' Health Insurance scheme, including employees of companies and their dependent family members. The dataset was provided by JMDC Inc. The annual age-adjusted prevalence of each disease was used to calculate the ratio of disease prevalence before and after the events. After the Fukushima disaster, hypertension, hyperlipidemia, and diabetes generally increased over a 9-year period in Fukushima Prefecture. The increase in the prevalence rate of these three NCDs and mental disorders were the highest among females aged 40-74 years compared to males and the other age groups. The prevalence of all four diseases increased after the COVID-19 outbreak in Japan, with marked increase in males aged 0-39 years. Populations that have experienced secondary health effects such as NCDs are unique to each disaster or pandemic, and it is important to provide tailor-made public health support among populations in accordance to the type of disasters and pandemic.

A prospective evaluation of AI-augmented epidemiology to forecast COVID-19 in the USA and Japan (preprint)

The COVID-19 pandemic has highlighted the global need for reliable models of disease spread. We propose an AI-augmented forecast modeling framework that provides daily predictions of the expected number of confirmed COVID-19 deaths, cases and hospitalizations during the following 4 weeks and we present an international, prospective evaluation of our models' performance across all states and counties in the USA and prefectures in Japan. National mean absolute percentage error (MAPE) for predicting COVID-19 associated deaths before and after prospective deployment remained consistently <2% (US) and <10% (Japan). Average statewide (US) and prefecture wide (Japan) MAPE was 6% and 26% respectively (14% when looking at prefectures with more than 10 deaths). We show that our models perform well even during periods of considerable change in population behavior, and that it is robust to demographic differences across different geographic locations. We further demonstrate that our framework provides meaningful explanatory insights with the models accurately adapting to local and national policy interventions. Our framework enables counterfactual simulations, which indicate continuing Non-Pharmaceutical Interventions alongside vaccinations is essential for faster recovery from the pandemic, delaying the application of interventions has a detrimental effect, and allow exploration of the consequences of different vaccination strategies. The COVID-19 pandemic remains a global emergency. In the face of substantial challenges ahead, the approach presented here has the potential to inform critical decisions.