Impact of COVID-19 on health services utilization in mainland China and its different regions based on S-ARIMA predictions

Global health services are disrupted by the COVID-19 pandemic. We evaluated extent and duration of impacts of the pandemic on health services utilization in different economically developed regions of mainland China. Based on monthly health services utilization data in China, we used Seasonal Autoregressive Integrated Moving Average (S-ARIMA) models to predict outpatient and emergency department visits to hospitals (OEH visits) per capita without pandemic. The impacts were evaluated by three dimensions:1) absolute instant impacts were evaluated by difference between predicted and actual OEH visits per capita in February 2020 and relative instant impacts were the ratio of absolute impacts to baseline OEH visits per capita;2) absolute and relative accumulative impacts from February 2020 to March 2021;3) duration of impacts was estimated by time that actual OEH visits per capita returned to its predicted value. From February 2020 to March 2021, the COVID-19 pandemic reduced OEH visits by 0.4676 per capita, equivalent to 659,453,647 visits, corresponding to a decrease of 15.52% relative to the pre-pandemic average annual level in mainland China. The instant impacts in central, northeast, east and west China were 0.1279, 0.1265, 0.1215, and 0.0986 visits per capita, respectively;and corresponding relative impacts were 77.63%, 66.16%, 44.39%, and 50.57%, respectively. The accumulative impacts in northeast, east, west and central China were up to 0.5898, 0.4459, 0.3523, and 0.3324 visits per capita, respectively;and corresponding relative impacts were 23.72%, 12.53%, 13.91%, and 16.48%, respectively. The OEH visits per capita has returned back to predicted values within the first 2, 6, 9, 9 months for east, central, west and northeast China, respectively. Less economically developed areas were affected for a longer time. Safe and equitable access to health services, needs paying great attention especially for undeveloped areas.

Optimizing Living Material Delivery During the COVID-19 Outbreak.

The coronavirus disease 2019 (COVID-19) epidemic has spread worldwide, posing a great threat to human beings. The stay-home quarantine is an effective way to reduce physical contacts and the associated COVID-19 transmission risk, which requires the support of efficient living materials (such as meats, vegetables, grain, and oil) delivery. Notably, the presence of potential infected individuals increases the COVID-19 transmission risk during the delivery. The deliveryman may be the medium through which the virus spreads among urban residents. However, traditional delivery route optimization methods don't take the virus transmission risk into account. Here, we propose a novel living material delivery route approach considering the possible COVID-19 transmission during the delivery. A complex network-based virus transmission model is developed to simulate the possible COVID-19 infection between urban residents and the deliverymen. A bi-objective model considering the COVID-19 transmission risk and the total route length is proposed and solved by the hybrid meta-heuristics integrating the adaptive large neighborhood search and simulated annealing. The experiment was conducted in Wuhan, China to assess the performance of the proposed approach. The results demonstrate that 935 vehicles will totally travel 56,424.55 km to deliver necessary living materials to 3,154 neighborhoods, with total risk [Formula: see text]. The presented approach reduces the risk of COVID-19 transmission by 67.55% compared to traditional distance-based optimization methods. The presented approach can facilitate a well response to the COVID-19 in the transportation sector.

Dynamic Imaging Changes of COVID-19 Pneumonia at Different Stages.

INTRODUCTION: To investigate the Computed Tomography (CT) imaging characteristics and dynamic changes of COVID-19 pneumonia at different stages. METHODS: Forty-six patients infected with COVID-19 who had chest CT scans were enrolled, and CT scans were performed 4-6 times with an interval of 2-5 days. RESULTS: At the early stage (n=25), ground glass opacity was presented in 11 patients (11/25 or 44.0 %) and ground glass opacity mixed with consolidation in 13 (13/25 or 52.0 %) in the lung CT images. At the progressive stage (n=38), ground glass opacity was presented in only one patient (1/38 or 2.6 %) and ground glass opacity mixed with consolidation in 33 (33/38 or 86.8 %). In the early improvement stage (n=38), the imaging presentation was ground glass opacity alone in three patients (3/38 or 7.9 %) and ground glass opacity mixed with consolidation in 34 (34/38 or 89.5 %). In the late improvement (absorption) stage (n=33), the primary imaging presentation was ground glass presentation in eight patients (8/33 or 24.2 %) and ground glass opacity mixed with consolidation in 23 (23/33 or 69.7 %). The lesion reached the peak at 4-16 days after disease onset, and 26 (26/38 or 68.4 %) patients reached the disease peak within ten days. Starting from 6 to 20 days after onset, the disease began to be improved, with 30 (30/38 or 78.9 %) patients being improved within 15 days. CONCLUSION: COVID-19 pneumonia will progress to the peak stage at a mediate time of seven days and enter the improvement stage at twelve days. Computed tomography imaging of the pulmonary lesion has a common pattern from disease onset to improvement and recovery and provides important information for evaluation of the disease course and treatment effect.

When did COVID-19 start? - Optimal inference of time ZERO (preprint)

According to the official report, the first case of COVID-19 and the first death in the United States occurred on January 20 and February 29, 2020, respectively. On April 21, California reported that the first death in the state occurred on February 6, implying that community spreading of COVID-19 might have started earlier than previously thought. Exactly what is time ZERO, i.e., when did COVID-19 emerge and begin to spread in the US and other countries? We develop a comprehensive predictive modeling framework to address this question. Using available data of confirmed infections to obtain the optimal values of the key parameters, we validate the model and demonstrate its predictive power. We then carry out an inverse inference analysis to determine time ZERO for ten representative States in the US, plus New York city, UK, Italy, and Spain. The main finding is that, in both the US and Europe, COVID-19 started around the new year day.

State-by-State prediction of likely COVID-19 scenarios in the United States and assessment of the role of testing and control measures (preprint)

Due to the heterogeneity among the States in the US, predicting COVID-19 trends and quantitatively assessing the effects of government testing capability and control measures need to be done via a State-by-State approach. We develop a comprehensive model for COVID-19 incorporating time delays and population movements. With key parameter values determined by empirical data, the model enables the most likely epidemic scenarios to be predicted for each State, which are indicative of whether testing services and control measures are vigorous enough to contain the disease. We find that government control measures play a more important role than testing in suppressing the epidemic. The vast disparities in the epidemic trends among the States imply the need for long-term placement of control measures to fully contain COVID-19.

Quantitative assessment of the role of undocumented infection in the 2019 novel coronavirus (COVID-19) pandemic (preprint)

An urgent problem in controlling COVID-19 spreading is to understand the role of undocumented infection. We develop a five-state model for COVID-19, taking into account the unique features of the novel coronavirus, with key parameters determined by the government reports and mathematical optimization. Tests using data from China, South Korea, Italy, and Iran indicate that the model is capable of generating accurate prediction of the daily accumulated number of confirmed cases and is entirely suitable for real-time prediction. The drastically disparate testing and diagnostic standards/policies among different countries lead to large variations in the estimated parameter values such as the duration of the outbreak, but such uncertainties have little effect on the occurrence time of the inflection point as predicted by the model, indicating its reliability and robustness. Model prediction for Italy suggests that insufficient government action leading to a large fraction of undocumented infection plays an important role in the abnormally high mortality in that country. With the data currently available from United Kingdom, our model predicts catastrophic epidemic scenarios in the country if the government did not impose strict travel and social distancing restrictions. A key finding is that, if the percentage of undocumented infection exceeds a threshold, a non-negligible hidden population can exist even after the the epidemic has been deemed over, implying the likelihood of future outbreaks should the currently imposed strict government actions be relaxed. This could make COVID-19 evolving into a long-term epidemic or a community disease a real possibility, suggesting the necessity to conduct universal testing and monitoring to identify the hidden individuals.