The Mann-Kendall-Sneyers test to identify the change points of COVID-19 time series in the United States.

BACKGROUND: One critical variable in the time series analysis is the change point, which is the point where an abrupt change occurs in chronologically ordered observations. Existing parametric models for change point detection, such as the linear regression model and the Bayesian model, require that observations are normally distributed and that the trend line cannot have extreme variability. To overcome the limitations of the parametric model, we apply a nonparametric method, the Mann-Kendall-Sneyers (MKS) test, to change point detection for the state-level COVID-19 case time series data of the United States in the early outbreak of the pandemic. METHODS: The MKS test is implemented for change point detection. The forward sequence and the backward sequence are calculated based on the new weekly cases between March 22, 2020 and January 31, 2021 for each of the 50 states. Points of intersection between the two sequences falling within the 95% confidence intervals are identified as the change points. The results are compared with two other change point detection methods, the pruned exact linear time (PELT) method and the regression-based method. Also, an open-access tool by Microsoft Excel is developed to facilitate the model implementation. RESULTS: By applying the MKS test to COVID-19 cases in the United States, we have identified that 30 states (60.0%) have at least one change point within the 95% confidence intervals. Of these states, 26 states have one change point, 4 states (i.e., LA, OH, VA, and WA) have two change points, and one state (GA) has three change points. Additionally, most downward changes appear in the Northeastern states (e.g., CT, MA, NJ, NY) at the first development stage (March 23 through May 31, 2020); most upward changes appear in the Western states (e.g., AZ, CA, CO, NM, WA, WY) and the Midwestern states (e.g., IL, IN, MI, MN, OH, WI) at the third development stage (November 19, 2020 through January 31, 2021). CONCLUSIONS: This study is among the first to explore the potential of the MKS test applied for change point detection of COVID-19 cases. The MKS test is characterized by several advantages, including high computational efficiency, easy implementation, the ability to identify the change of direction, and no assumption for data distribution. However, due to its conservative nature in change point detection and moderate agreement with other methods, we recommend using the MKS test primarily for initial pattern identification and data pruning, especially in large data. With modification, the method can be further applied to other health data, such as injuries, disabilities, and mortalities.

Enhancing long-term forecasting: Learning from COVID-19 models.

While much effort has gone into building predictive models of the COVID-19 pandemic, some have argued that early exponential growth combined with the stochastic nature of epidemics make the long-term prediction of contagion trajectories impossible. We conduct two complementary studies to assess model features supporting better long-term predictions. First, we leverage the diverse models contributing to the CDC repository of COVID-19 USA death projections to identify factors associated with prediction accuracy across different projection horizons. We find that better long-term predictions correlate with: (1) capturing the physics of transmission (instead of using black-box models); (2) projecting human behavioral reactions to an evolving pandemic; and (3) resetting state variables to account for randomness not captured in the model before starting projection. Second, we introduce a very simple model, SEIRb, that incorporates these features, and few other nuances, offers informative predictions for as far as 20-weeks ahead, with accuracy comparable with the best models in the CDC set. Key to the long-term predictive power of multi-wave COVID-19 trajectories is capturing behavioral responses endogenously: balancing feedbacks where the perceived risk of death continuously changes transmission rates through the adoption and relaxation of various Non-Pharmaceutical Interventions (NPIs).

Comparison of Predictors of COVID-19 Vaccination Intention Between Healthcare Workers and Non-Healthcare Workers in China.

BACKGROUND: Vaccination is an effective strategy to mitigate the spread of COVID-19. This study aimed to compare predictors of vaccination intention between healthcare workers (HCWs) and non-healthcare workers (non-HCWs) in China. METHODS: A web-based cross-sectional survey was conducted among HCWs and non-HCWs. Several bivariate analysis techniques, eg, crosstab with Chi-square, independent t-test and single factor ANOVA, were performed to analyze the correlation. After that, a series of multivariate binary regressions were employed to determine predictors of vaccination intention. RESULTS: Intention was closely and significantly related with gender, perceived vaccination knowledge, perceived importance and effectiveness of vaccine to prevent COVID-19. HCWs and non-HCWs were heterogeneous, since vaccination intention, perceived knowledge, and attitudes (eg, importance, severity, risk) toward COVID-19 or vaccine had statistically significant difference between the two groups. With comparison of predictors of vaccination intention, for HCWs, demographic factors were the major predictors of COVID-19 vaccination intention. Female HCWs and HCWs with a Master's or higher degree were more hesitant about vaccination (P = 0.01 and P < 0.001, respectively), while HCWs had greater vaccination intention as their age increased (P = 0.02). For non-HCWs, perceived vaccination knowledge was the major predictor of COVID-19 vaccination intention (P < 0.001). Additionally, perceived importance and effectiveness of vaccine were predictors for both HCWs and non-HCWs. CONCLUSION: Vaccination intention of HCWs was greater than that of non-HCWs in China. Measures should be taken to improve the vaccination rate based on the predictors of vaccination intention identified in this study. For HCWs, especially those with a high level of education or who were females, the safety and effectiveness of vaccines in use may reinforce their vaccination intention. For non-HCWs, popularization of general medical knowledge, including of vaccine-preventable diseases, may increase their vaccination intention.

Weather, air pollution, and SARS-CoV-2 transmission: a global analysis.

BACKGROUND: Understanding how environmental factors affect SARS-CoV-2 transmission could inform global containment efforts. Despite high scientific and public interest and multiple research reports, there is currently no consensus on the association of environmental factors and SARS-CoV-2 transmission. To address this research gap, we aimed to assess the relative risk of transmission associated with weather conditions and ambient air pollution. METHODS: In this global analysis, we adjusted for the delay between infection and detection, estimated the daily reproduction number at 3739 global locations during the COVID-19 pandemic up until late April, 2020, and investigated its associations with daily local weather conditions (ie, temperature, humidity, precipitation, snowfall, moon illumination, sunlight hours, ultraviolet index, cloud cover, wind speed and direction, and pressure data) and ambient air pollution (ie, PM2·5, nitrogen dioxide, ozone, and sulphur dioxide). To account for other confounding factors, we included both location-specific fixed effects and trends, controlling for between-location differences and heterogeneities in locations' responses over time. We built confidence in our estimations through synthetic data, robustness, and sensitivity analyses, and provided year-round global projections for weather-related risk of global SARS-CoV-2 transmission. FINDINGS: Our dataset included data collected between Dec 12, 2019, and April 22, 2020. Several weather variables and ambient air pollution were associated with the spread of SARS-CoV-2 across 3739 global locations. We found a moderate, negative relationship between the estimated reproduction number and temperatures warmer than 25°C (a decrease of 3·7% [95% CI 1·9-5·4] per additional degree), a U-shaped relationship with outdoor ultraviolet exposure, and weaker positive associations with air pressure, wind speed, precipitation, diurnal temperature, sulphur dioxide, and ozone. Results were robust to multiple assumptions. Independent research building on our estimates provides strong support for the resulting projections across nations. INTERPRETATION: Warmer temperature and moderate outdoor ultraviolet exposure result in a slight reduction in the transmission of SARS-CoV-2; however, changes in weather or air pollution alone are not enough to contain the spread of SARS-CoV-2 with other factors having greater effects. FUNDING: None.

Cangma Huadu granules, a new drug with great potential to treat coronavirus and influenza infections, exert its efficacy through anti-inflammatory and immune regulation.

ETHNOPHARMACOLOGICAL RELEVANCE: Coronavirus and influenza virus infection seriously threaten human health. Cangma Huadu Granules (CMHD) is an in-hospital preparation composed of eight traditional Chinese medicines (TCM), which has been clinically used against COVID-19 in China and may be a promising candidate for the treatment of influenza. However, the role of its treatment urgently needs to be studied. AIM OF THE STUDY: To evaluate the therapeutic effects of CMHD on pneumonia induced by coronavirus (HCoV-229E) and influenza A virus (H1N1/FM1) in mice and explore its mechanism of anti-infection. MATERIALS AND METHODS: Mice were infected with HCoV-229E or H1N1/FM1 virus through the nasal cavity. CMHD (12.1, 6.05 and 3.03 g/kg/d) or the positive control drugs were administered intragastrically. The lung index and histopathological changes were used to evaluate the therapeutic effect of CMHD. The expression of TNF-α, IL-1ß, IL-6 and IL-4 in Serum and the proportion of CD4+ and CD8+ T lymphocytes in peripheral blood were detected to evaluate the anti-inflammatory and immune regulation effects of CMHD, respectively. Furthermore, the levels of p-NF-κBp65/ NF-κB p65, which was the key targets of the NF-κB pathway was analyzed. RESULTS: In HCoV-229E-induced pneumonia, the lung index was markedly reduced, and lung pathology was improved in mice that treated with CMHD (12.1, 6.05 g/kg/d). Meanwhile, the expression of TNF-α, IL-6 were obviously inhibited, but the expression of IL-4 was significantly increased in CMHD groups. Compared with the model group, CMHD could also markedly upregulate the level of CD4+ and CD8+. Furthermore, CMHD has a markedly effect on inhibit the expression of p-NF-κB p65/NF-κB p65 in the lung. In H1N1-induced pneumonia, the lung index of mice in the CMHD (12.1 g/kg/d) treatment group was lower than that in the model group, and less inflammatory infiltration could be seen in the lung pathological. Moreover, CMHD could also obviously decrease the expression of TNF-α, IL-1ß, IL-6, but significantly increase the expression of IL-4. Except for that, CMHD could also markedly downregulate the level of CD4+ and upregulate the level of CD8+ compared with the model group. In addition, CMHD has a markedly effect on inhibit the expression of p-NF-κB p65/NF-κB p65 in the lung. CONCLUSION: CMHD can significantly combats viral infections caused by HCoV-229E and H1N1, and the mechanism may be related to its multiple functions of anti-inflammatory, immunity regulating and inhibiting NF-κB signal transduction pathway.

Health inequities in COVID-19 vaccination among the elderly: Case of Connecticut.

BACKGROUND: In the United States, distribution plans for the COVID-19 vaccination were established at the state level. However, some states, such as Connecticut, followed an age-based strategy without considering occupations or co-morbid conditions due to its simplicity in implementation. This strategy raised concerns about exacerbating health inequities because it did not prioritize vulnerable communities, specifically, minorities and low-income groups. The study aims to examine the vaccination inequities among different population groups for people aged 65+. METHODS: A cross-sectional analysis of quantile-based independent sample t-test was employed to examine the relationship between eight social vulnerability indices (SVIs, i.e., below poverty, unemployed, without high school diploma, disability, minority, speaks English less than well, no vehicle, and mobile homes) and vaccination rates at the town level in Connecticut during the second phase of the vaccine distribution plan when individuals aged 65 and over were eligible. Negative binomial regressions were employed to further justify the relationships between SVIs and vaccination rates. RESULTS: The report shows that the differences in vaccination rates were statistically significant between the most vulnerable and the least vulnerable towns with respect to six SVIs (i.e., below poverty, without high school diploma, disability, minority, speaks English less than well, and no vehicle). The vaccination gap was greater for people aged 75+ than people aged 65-74. Among the selected SVIs, below poverty was negatively correlated with the vaccination rate for 75+, and without high school diploma was negatively correlated with both rates. CONCLUSIONS: This report reveals the significant health inequities in COVID-19 vaccination among the elderly population at the early vaccination phase. It can shed insights into health policy initiatives to improve vaccination coverage in the elderly communities, such as promoting onsite scheduling and increasing at-home vaccination services.

Hypothetical case replacement can be used to quantify the robustness of trial results.

OBJECTIVES: We apply a general case replacement framework for quantifying the robustness of causal inferences to characterize the uncertainty of findings from clinical trials. STUDY DESIGN AND SETTING: We express the robustness of inferences as the amount of data that must be replaced to change the conclusion and relate this to the fragility of trial results used for dichotomous outcomes. We illustrate our approach in the context of an RCT of hydroxychloroquine on pneumonia in COVID-19 patients and a cumulative meta-analysis of the effect of antihypertensive treatments on stroke. RESULTS: We developed the Robustness of an Inference to Replacement (RIR), which quantifies how many treatment cases with positive outcomes would have to be replaced with hypothetical patients who did not receive a treatment to change an inference. The RIR addresses known limitations of the Fragility Index by accounting for the observed rates of outcomes. It can be used for varying thresholds for inference, including clinical importance. CONCLUSION: Because the RIR expresses uncertainty in terms of patient experiences, it is more relatable to stakeholders than P-values alone. It helps identify when results are statistically significant, but conclusions are not robust, while considering the rareness of events in the underlying data.