Internet search data could Be used as novel indicator for assessing COVID-19 epidemic

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

BACKGROUND

Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts.

OBJECTIVE

This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods.

METHODS

A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers.

RESULTS

Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses.

CONCLUSIONS

The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

The lead time and geographical variations of Baidu Search Index in the early warning of COVID-19

Internet search data was a useful tool in the pre-warning of COVID-19. However, the lead time and indicators may change over time and space with the new variants appear and massive nucleic acid testing. Since Omicron appeared in late 2021, we collected the daily number of cases and Baidu Search Index (BSI) of seven search terms from 1 January to 30 April, 2022 in 12 provinces/prefectures to explore the variation in China. Two search peaks of "COVID-19 epidemic", "Novel Coronavirus" and "COVID-19" can be observed. One in January, which showed 3 days lead time in Henan and Tianjin. Another on early March, which occurred 0-28 days ahead of the local epidemic but the lead time had spatial variation. It was 4 weeks in Shanghai, 2 weeks in Henan and 5-8 days in Jilin Province, Jilin and Changchun Prefecture. But it was only 1-3 days in Tianjin, Quanzhou Prefecture, Fujian Province and 0 day in Shenzhen, Shandong Province, Qingdao and Yanbian Prefecture. The BSI was high correlated (rs:0.70-0.93) to the number of cases with consistent epidemiological change trend. The lead time of BSI had spatial and temporal variation and was close related to the strength of nucleic acid testing. The case detection ability should be strengthened when perceiving BSI increase.

Quantitative analysis of the impact of various urban socioeconomic indicators on search-engine-based estimation of COVID-19 prevalence

Numerous studies have proposed search engine-based estimation of COVID-19 prevalence during the COVID-19 pandemic; however, their estimation models do not consider the impact of various urban socioeconomic indicators (USIs). This study quantitatively analysed the impact of various USIs on search engine-based estimation of COVID-19 prevalence using 15 USIs (including total population, gross regional product (GRP), and population density) from 369 cities in China. The results suggested that 13 USIs affected either the correlation (SC-corr) or time lag (SC-lag) between search engine query volume and new COVID-19 cases ( p <0.05). Total population and GRP impacted SC-corr considerably, with their correlation coefficients r for SC-corr being 0.65 and 0.59, respectively. Total population, GRP per capita, and proportion of the population with a high school diploma or higher had simultaneous positive impacts on SC-corr and SC-lag ( p <0.05); these three indicators explained 37-50% of the total variation in SC-corr and SC-lag. Estimations for different urban agglomerations revealed that the goodness of fit,R 2 , for search engine-based estimation was more than 0.6 only when total urban population, GRP per capita, and proportion of the population with a high school diploma or higher exceeded 11.08 million, 120,700, and 38.13%, respectively. A greater urban size indicated higher accuracy of search engine-based estimation of COVID-19 prevalence. Therefore, the accuracy and time lag for search engine-based estimation of infectious disease prevalence can be improved only when the total urban population, GRP per capita, and proportion of the population with a high school diploma or higher are greater than the aforementioned thresholds.

Spatial association between COVID-19 incidence rate and nighttime light index

This study statistically identified the localised association between socioeconomic conditions and the coronavirus disease 2019 (COVID-19) incidence rate in Thailand on the basis of the 1,727,336 confirmed cases reported nationwide during the first major wave of the pandemic (March-May 2020) and the second one (July 2021-September 2021). The nighttime light (NTL) index, formulated using satellite imagery, was used as a provincial proxy of monthly socioeconomic conditions. Local indicators of spatial association statistics were applied to identify the localised bivariate association between COVID-19 incidence rate and the year-on-year change of NTL index. A statistically significant negative association was observed between the COVID-19 incidence rate and the NTL index in some central and southern provinces in both major pandemic waves. Regression analyses were also conducted using the spatial lag model (SLM) and the spatial error model (SEM). The obtained slope coefficient, for both major waves of the pandemic, revealed a statistically significant negative association between the year-on-year change of NTL index and COVID-19 incidence rate (SLM: coefficient= âˆ'0.0078 and âˆ'0.0064 with P<0.001 and 0.056, respectively; and SEM: coefficient= âˆ'0.0086 and âˆ'0.0083 with P=0.067 and 0.056, respectively). All of the obtained results confirmed the negative association between the COVID-19 pandemic and socioeconomic activity revealing the future extensive applications of satellite imagery as an alternative data source for the timely monitoring of the multidimensional impacts of the pandemic.

Factors that most expose countries to COVID-19: a composite indicators-based approach

Studies carried out in different countries correlate social, economic, environmental, and health factors with the number of cases and deaths from COVID-19. However, such studies do not reveal which factors make one country more exposed to COVID-19 than other. Based on the composite indicators approach, this research identifies the factors that most impact the number of cases and deaths of COVID-19 worldwide and measures countries' exposure to COVID-19. Three composite indicators of exposure to COVID-19 were constructed through Principal Component Analysis, Simple Additive Weighting, and k-means clustering. The number of cases and deaths from COVID-19 is strongly correlated ( R  > 0.60) with composite indicator scores and moderately concordant ( K  > 0.4) with country clusters. Factors directly or indirectly associated with the age of the population are the ones that most expose countries to COVID-19. The population of countries most exposed to COVID-19 is 12 years older on average. The proportion of the elderly population in these countries is at least twice that of countries less exposed to COVID-19. Factors that can increase the population's life expectancy, such as Gross Domestic Product per capita and the Human Development Index, are four times and 1.3 times higher in more exposed countries to COVID-19. Providing better living conditions increases both the population's life expectancy and the country's exposure to COVID-19.

Efficacy of limited antiviral treatment, testing, hospitalization, and social distancing for COVID-19 pandemic

The control measures of a pandemic must be cautiously evaluated, especially when resources are “limited”. A model of COVID-19 transmission dynamics is applied to assess the impact of antiviral treatment, testing, hospitalization, and social distancing. Under the assumption of “unlimited” resources, five control strategies involving social distancing, testing, hospitalization, and antiviral treatment are tested. Then these “optimal” policies are sought in the case of limited resources on behalf of a COVID-19 pandemic scenario. The amplitude of peak epidemics will often be minimized by executing strategies from the beginning of a pandemic, spreading the epidemics’ greatest impact over a longer time frame. Therefore, the timing and potency of control measures can reduce the pressure on the system during the top of the epidemic through the pandemic, decreasing the pressure on the healthcare infrastructure. In case of limited access to antiviral supplies, the role of testing, hospitalization, and social distancing strategies is emphasized in this study.

Role of ACE2 receptor and the landscape of treatment options from convalescent plasma therapy to the drug repurposing in COVID-19

Since the first case reports in Wuhan, China, the SARS-CoV-2 has caused a pandemic and took lives of > 8,35,000 people globally. This single-stranded RNA virus uses Angiotensin-converting enzyme 2 (ACE2) as a receptor for entry into the host cell. Overexpression of ACE2 is mainly observed in hypertensive, diabetic and heart patients that make them prone to SARS-CoV-2 infection. Mitigations strategies were opted globally by the governments to minimize transmission of SARS-CoV-2 via the implementation of social distancing norms, wearing the facemasks, and spreading awareness using digital platforms. The lack of an approved drug treatment regimen, and non-availability of a vaccine, collectively posed a challenge for mankind to fight against the SARS-CoV-2 pandemic. In this scenario, repurposing of existing drugs and old treatment options like convalescent plasma therapy can be one of the potential alternatives to treat the disease. The drug repurposing provides a selection of drugs based on the scientific rationale and with a shorter cycle of clinical trials, while plasma isolated from COVID-19 recovered patients can be a good source of neutralizing antibody to provide passive immunity. In this review, we provide in-depth analysis on these two approaches currently opted all around the world to treat COVID-19 patients. For this, we used "Boolean Operators" such as AND, OR & NOT to search relevant research articles/reviews from the PUBMED for the repurposed drugs and the convalescent plasma in the COVID-19 treatment. The repurposed drugs like Chloroquine and Hydroxychloroquine, Tenofovir, Remdesivir, Ribavirin, Darunavir, Oseltamivir, Arbidol (Umifenovir), Favipiravir, Anakinra, and Baricitinib are already being used in clinical trials to treat the COVID-19 patients. These drugs have been approved for a different indication and belong to a diverse category such as anti-malarial/anti-parasitic, anti-retroviral/anti-viral, anti-cancer, or against rheumatoid arthritis. Although, the vaccine would be an ideal option for providing active immunity against the SARS-CoV-2, but considering the current situation, drug repurposing and convalescent plasma therapy and repurposed drugs are the most viable option against SARS-CoV-2.