Estimation of the Actual Incidence of Coronavirus Disease (COVID-19) in Emergent Hotspots: The Example of Hokkaido, Japan during February-March 2020

Mobile phone data reveals spatiotemporal recreational patterns in conservation areas during the COVID pandemic

Understanding visitation patterns is crucial in developing effective conservation strategies for protected areas, as it serves as an indicator for operating an ecosystem management plan that balances biodiversity and ecosystem services intertwined with public health and social benefits. However, limited data availability during the COVID-19 pandemic has hindered the comprehensive understanding of temporal changes in realized cultural ecosystem services, particularly in recreational activities within these areas. Our study utilized GPS data from mobile phones to quantify visitor characteristics and their contribution to recreational ecosystem services in protected areas at a national scale during the COVID-19 pandemic. We estimated the pandemic's relative impact on visitor patterns at 98 visitor centers in national parks and Ramsar sites in Japan. The total number of visitors and travel distance in various sizes of protected areas decreased after the outbreak of COVID-19. The number of visitors in the protected areas displayed a quick recovery despite the increasing positive COVID-19 cases during the following summer. Post-pandemic, visitors showed a preference for less densely populated protected areas closer to their home range. Our findings partly suggest that protecting a diverse range of conservation areas along the urban gradient could be an effective strategy for maintaining the resilience of recreational services during a prolonged pandemic.

A survey on the correlation between PM2.5 concentration and the incidence of suspected and positive cases of COVID-19 referred to medical centers: A case study of Tehran

COVID-19, one of the greatest health challenges of the present century, has infected millions of people and caused more than 6 million deaths worldwide. The causative agent of this disease is the new virus SARS-CoV-2; which continues to spread globally and sometimes with new and more complex aspects than before. The present study is an observational study aimed to investigate the role of AQI; PM_2.5 and its relationship with the incidence of suspected cases (SC) and positive cases (PC) of COVID-19 at different levels of the air quality index (AQI) in Tehran, the capital of Iran in the period from Feb 20th, 2020 to Feb 22nd, 2021. Data on AQI were collected online from the air monitoring website of Air Quality Control Company under the supervision of Tehran Municipality. The data on suspected and positive cases were obtained from the Iranian Ministry of Health. The results and statistical analysis (Pearson correlation test) showed that with the increase of AQI level, the number of suspected cases (SC) and positive cases (PC), also increased (P-value<0.01). The average daily number of suspected and positive COVID-19 cases referred to medical centers, at different levels of the AQI was as follows: level II: yellow, moderate (SC: N_ave = 466; PC: N_ave = 223), level III: orange, unhealthy for sensitive groups (SC: N_ave = 564; PC: N_ave = 275), and Level IV: red, unhealthy (SC: N_ave = 558; PC: N_ave = 294). The results of the GEE for seasonal comparison (winter as reference season), showed that there is an epidemiological pattern in autumn with colder weather compared to other seasons in both suspected (Cl: %95, B = 408.94) and positive (Cl: %95, B = 83.42) cases of COVID-19. The results of this study will serve policymakers as an informative tool for guidance on the importance of the role of air pollution in viral epidemics.

Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic

Background: Forecasting the behavior of epidemic outbreaks is vital in public health. This makes it possible to anticipate the planning and organization of the health system, as well as possible restrictive or preventive measures. During the COVID-19 pandemic, this need for prediction has been crucial. This paper attempts to characterize the alternative models that were applied in the first wave of this pandemic context, trying to shed light that could help to understand them for future practical applications. Methods: A systematic literature search was performed in standardized bibliographic repertoires, using keywords and Boolean operators to refine the findings, and selecting articles according to the main PRISMA 2020 statement recommendations. Results: After identifying models used throughout the first wave of this pandemic (between March and June 2020), we begin by examining standard data-driven epidemiological models, including studies applying models such as SIR (Susceptible-Infected-Recovered), SQUIDER, SEIR, time-dependent SIR, and other alternatives. For data-driven methods, we identify experiences using autoregressive integrated moving average (ARIMA), evolutionary genetic programming machine learning, short-term memory (LSTM), and global epidemic and mobility models. Conclusions: The COVID-19 pandemic has led to intensive and evolving use of alternative infectious disease prediction models. At this point it is not easy to decide which prediction method is the best in a generic way. Moreover, although models such as the LSTM emerge as remarkably versatile and useful, the practical applicability of the alternatives depends on the specific context of the underlying variable and on the information of the target to be prioritized. In addition, the robustness of the assessment is conditioned by heterogeneity in the quality of information sources and differences in the characteristics of disease control interventions. Further comprehensive comparison of the performance of models in comparable situations, assessing their predictive validity, is needed. This will help determine the most reliable and practical methods for application in future outbreaks and eventual pandemics.

Epidemiology of coronavirus disease 2019 (COVID-19) in Japan during the first and second waves

Following the emergence and worldwide spread of coronavirus disease 2019 (COVID-19), each country has attempted to control the disease in different ways. The first patient with COVID-19 in Japan was diagnosed on 15 January 2020, and until 31 October 2020, the epidemic was characterized by two large waves. To prevent the first wave, the Japanese government imposed several control measures such as advising the public to avoid the 3Cs (closed spaces with poor ventilation, crowded places with many people nearby, and close-contact settings such as close-range conversations) and implementation of "cluster buster" strategies. After a major epidemic occurred in April 2020 (the first wave), Japan asked its citizens to limit their numbers of physical contacts and announced a non-legally binding state of emergency. Following a drop in the number of diagnosed cases, the state of emergency was gradually relaxed and then lifted in all prefectures of Japan by 25 May 2020. However, the development of another major epidemic (the second wave) could not be prevented because of continued chains of transmission, especially in urban locations. The present study aimed to descriptively examine propagation of the COVID-19 epidemic in Japan with respect to time, age, space, and interventions implemented during the first and second waves. Using publicly available data, we calculated the effective reproduction number and its associations with the timing of measures imposed to suppress transmission. Finally, we crudely calculated the proportions of severe and fatal COVID-19 cases during the first and second waves. Our analysis identified key characteristics of COVID-19, including density dependence and also the age dependence in the risk of severe outcomes. We also identified that the effective reproduction number during the state of emergency was maintained below the value of 1 during the first wave.

Spatiotemporal patterns of the COVID-19 epidemic in Mexico at the municipality level

In recent history, Coronavirus Disease 2019 (COVID-19) is one of the worst infectious disease outbreaks affecting humanity. The World Health Organization has defined the outbreak of COVID-19 as a pandemic, and the massive growth of the number of infected cases in a short time has caused enormous pressure on medical systems. Mexico surpassed 3.7 million confirmed infections and 285,000 deaths on October 23, 2021. We analysed the spatio-temporal patterns of the COVID-19 epidemic in Mexico using the georeferenced confirmed cases aggregated at the municipality level. We computed weekly Moran’s I index to assess spatial autocorrelation over time and identify clusters of the disease using the “flexibly shaped spatial scan” approach. Finally, we compared Euclidean, cost, resistance distances and gravitational model to select the best-suited approach to predict inter-municipality contagion. We found that COVID-19 pandemic in Mexico is characterised by clusters evolving in space and time as parallel epidemics. The gravitational distance was the best model to predict newly infected municipalities though the predictive power was relatively low and varied over time. This study helps us understand the spread of the epidemic over the Mexican territory and gives insights to model and predict the epidemic behaviour.

Impact of insufficient detection in COVID-19 outbreaks

The COVID-19 (novel coronavirus disease 2019) pandemic has tremendously impacted global health and economics. Early detection of COVID-19 infections is important for patient treatment and for controlling the epidemic. However, many countries/regions suffer from a shortage of nucleic acid testing (NAT) due to either resource limitations or epidemic control measures. The exact number of infective cases is mostly unknown in counties/regions with insufficient NAT, which has been a major issue in predicting and controlling the epidemic. In this paper, we propose a mathematical model to quantitatively identify the influences of insufficient detection on the COVID-19 epidemic. We extend the classical SEIR (susceptible-exposed-infections-recovered) model to include random detections which are described by Poisson processes. We apply the model to the epidemic in Guam, Texas, the Virgin Islands, and Wyoming in the United States and determine the detection probabilities by fitting model simulations with the reported number of infected, recovered, and dead cases. We further study the effects of varying the detection probabilities and show that low level-detection probabilities significantly affect the epidemic; increasing the detection probability of asymptomatic infections can effectively reduce the the scale of the epidemic. This study suggests that early detection is important for the control of the COVID-19 epidemic.

Plant-Based Phytochemical Screening by Targeting Main Protease of SARS-CoV-2 to Design Effective Potent Inhibitors

Currently, a worldwide pandemic has been declared in response to the spread of coronavirus disease 2019 (COVID-19), a fatal and fast-spreading viral infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The low availability of efficient vaccines and treatment options has resulted in a high mortality rate, bringing the world economy to its knees. Thus, mechanistic investigations of drugs capable of counteracting this disease are in high demand. The main protease (Mpro) expressed by SARS-CoV-2 has been targeted for the development of potential drug candidates due to the crucial role played by Mpro in viral replication and transcription. We generated a phytochemical library containing 1672 phytochemicals derived from 56 plants, which have been reported as having antiviral, antibacterial, and antifungal activity. A molecular docking program was used to screen the top three candidate compounds: epicatechin-3-O-gallate, psi-taraxasterol, and catechin gallate, which had respective binding affinities of -8.4, -8.5, and -8.8 kcal/mol. Several active sites in the targeted protein, including Cys145, His41, Met49, Glu66, and Met165, were found to interact with the top three candidate compounds. The multiple simulation profile, root-mean-square deviation, root-mean-square fluctuation, radius of gyration, and solvent-accessible surface area values supported the inflexible nature of the docked protein-compound complexes. The toxicity and carcinogenicity profiles were assessed, which showed that epicatechin-3-O-gallate, psi-taraxasterol, and catechin gallate had favorable pharmacological properties with no adverse effects. These findings suggest that these compounds could be developed as part of an effective drug development pathway to treat COVID-19.