Impact of non-pharmaceutical interventions on the COVID-19 epidemic: A modelling study

Impact of Non-pharmaceutical Interventions on the Control of COVID-19 in Iran: A Mathematical Modeling Study

BACKGROUND

During the first months of the coronavirus disease 2019 (COVID-19) pandemic, Iran reported high numbers of infections and deaths. In the following months, the burden of this infection decreased significantly, possibly due to the impact of a package of interventions. We modeled the dynamics of COVID-19 infection in Iran to quantify the impacts of these interventions.

METHODS

We used a modified susceptible-exposed-infected-recovered (SEIR) model to model the COVID-19 epidemic in Iran, from January 21, 2020 to September 21, 2020. We estimated the 95% uncertainty intervals (UIs) using Markov chain Monte Carlo simulation. Under different scenarios, we assessed the effectiveness of non-pharmaceutical interventions (NPIs) including physical distancing measures and self-isolation. We also estimated the time-varying reproduction number (Rt ), using our mathematical model and epidemiologic data.

RESULTS

If no NPIs were applied, there could have been a cumulative number of 51 800 000 (95% UI: 1 910 000- 77 600 000) COVID-19 infections and 266 000 (95% UI: 119 000-476 000) deaths by September 21, 2020. If physical distancing interventions, such as school/border closures and self-isolation interventions had been introduced a week earlier than they were actually launched, 30.8% and 35.2% reduction in the number of deaths and infections respectively could have been achieved by September 21, 2020. The observed daily number of deaths showed that the Rt was one or more than one almost every day during the analysis period.

CONCLUSION

Our models suggest that the NPIs implemented in Iran between January 21, 2020 and September 21, 2020 had significant effects on the spread of the COVID-19 epidemic. Our study also showed that the timely implementation of NPIs showed a profound effect on further reductions in the numbers of infections and deaths. This highlights the importance of forecasting and early detection of future waves of infection and of the need for effective preparedness and response capabilities.

Estimating the Number of COVID-19 Cases and Impact of New COVID-19 Variants and Vaccination on the Population in Kerman, Iran: A Mathematical Modeling Study

COVID-19 is spreading all over Iran, and Kerman is one of the most affected cities. We conducted this study to predict COVID-19-related deaths, hospitalization, and infected cases under different scenarios (scenarios A, B, and C) by 31 December 2021 in Kerman. We also aimed to assess the impact of new COVID-19 variants and vaccination on the total number of COVID-19 cases, deaths, and hospitalizations (scenarios D, E, and F) using the modified susceptible-exposed-infected-removed (SEIR) model. We calibrated the model using deaths reported from the start of the epidemic to August 30, 2021. A Monte Carlo Markov Chain (MCMC) uncertainty analysis was used to estimate 95% uncertainty intervals (UI). We also calculated the time-varying reproductive number (R_t) following time-dependent methods. Under the worst-case scenario (scenario A; contact rate = 10, self‐isolation rate = 30%, and average vaccination shots per day = 5,000), the total number of infections by December 31, 2021, would be 1,625,000 (95% UI: 1,112,000–1,898,000) with 6,700 deaths (95% UI: 5,200–8,700). With the presence of alpha and delta variants without vaccine (scenario D), the total number of infected cases and the death toll were estimated to be 957,000 (95% UI: 208,000–1,463,000) and 4,500 (95% UI: 1,500–7,000), respectively. If 70% of the population were vaccinated when the alpha variant was dominant (scenario E), the total number of infected cases and deaths would be 608,000 (95% UI: 122,000–743,000) and 2,700 (95% UI: 700–4,000), respectively. The R_t was ≥1 almost every day during the epidemic. Our results suggest that policymakers should concentrate on improving vaccination and interventions, such as reducing social contacts, stricter limitations for gathering, public education to promote social distancing, incensing case finding and contact tracing, effective isolation, and quarantine to prevent more COVID-19 cases, hospitalizations, and deaths in Kerman.

Feature Importance Analysis by Nowcasting Perspective to Predict COVID-19

The present work raises an investigation about prediction and the feature importance to estimate the COVID-19 infection, using Machine Learning approach. Our work analyzed the inclusion of climatic features, mobility, government actions and the number of cases per health sub-territory from an existing model. The Random Forest with Permutation Importance method was used to assess the importance and list the thirty most relevant that represent the probability of infection of the disease. Among all features, the most important were: i) the variables per region health stand out, ii) period comprised between the date of notification and symptom onset, iii) symptoms features as fever, cough and sore throat, iv) variables of the traffic flow and mobility, and also v) wheathers features. The model was validated and reached an accuracy average of 81.82%, whereas the sensitivity and specificity achieved 87.52% and the 78.67% respectively in the infection estimate. Therefore, the proposed investigation represents an alternative to guide authorities in understanding aspects related to the disease.

Government Intervention, Human Mobility, and COVID-19: A Causal Pathway Analysis from 121 Countries

Based on data from 121 countries, the study assesses the dynamic effect and causality path of the government epidemic prevention policies and human mobility behaviors on the growth rates of COVID-19 new cases and deaths. Our results find that both policies and behaviors influenced COVID-19 cases and deaths. The direct effect of policies on COVID-19 was more than the indirect effect. Policies influence behaviors, and behaviors react spontaneously to information. Further, masks give people a false sense of security and increase mobility. The close public transport policy increased COVID-19 new cases. We also conducted sensitivity analysis and found that some policies hold robustly, such as the policies of school closing, restrictions on gatherings, stay-at-home requirements, international travel controls, facial coverings, and vaccination. The counterfactual tests suggest that, as of early March 2021, if governments had mandated masking policies early in the epidemic, the cases and deaths would have been reduced by 18% and 14% separately. If governments had implemented vaccination policies early in the pandemic, the cases and deaths would have been reduced by 93% and 62%, respectively. Without public transportation closures, cases and deaths would have been reduced by 40% and 10%, respectively.

Impact of Nonpharmaceutical Interventions on ICU Admissions During Lockdown for Coronavirus Disease 2019 in New Zealand-A Retrospective Cohort Study

OBJECTIVES

Nonpharmaceutical interventions are implemented internationally to mitigate the spread of severe acute respiratory syndrome coronavirus 2 with the aim to reduce coronavirus disease 2019-related deaths and to protect the health system, particularly intensive care facilities from being overwhelmed. The aim of this study is to describe the impact of nonpharmaceutical interventions on ICU admissions of non-coronavirus disease 2019-related patients.

DESIGN

Retrospective cohort study.

SETTING

Analysis of all reported adult patient admissions to New Zealand ICUs during Level 3 and Level 4 lockdown restrictions from March 23, to May 13, 2020, in comparison with equivalent periods from 5 previous years (2015-2019).

SUBJECTS

Twelve-thousand one-hundred ninety-two ICU admissions during the time periods of interest were identified.

MEASUREMENTS

Patient data were obtained from the Australian and New Zealand Intensive Care Society Adult Patient Database, Australian and New Zealand Intensive Care Society critical care resources registry, and Statistics New Zealand. Study variables included patient baseline characteristics and ICU resource use.

MAIN RESULTS

Nonpharmaceutical interventions in New Zealand were associated with a 39.1% decrease in ICU admission rates (p < 0.0001). Both elective (-44.2%) and acute (-36.5%) ICU admissions were significantly reduced when compared with the average of the previous 5 years (both p < 0.0001). ICU occupancy decreased from a mean of 64.3% (2015-2019) to 39.8% in 2020. Case mix, ICU resource use per patient, and ICU and hospital mortality remained unchanged.

CONCLUSIONS

The institution of nonpharmaceutical interventions was associated with a significant decrease in elective and acute ICU admissions and ICU resource use. These findings may help hospitals and health authorities planning for surge capacities and elective surgery management in future pandemics.

Effectiveness of potential antiviral treatments in COVID-19 transmission control: a modelling study

BACKGROUND

Novel coronavirus disease 2019 (COVID-19) causes an immense disease burden. Although public health countermeasures effectively controlled the epidemic in China, non-pharmaceutical interventions can neither be maintained indefinitely nor conveniently implemented globally. Vaccination is mainly used to prevent COVID-19, and most current antiviral treatment evaluations focus on clinical efficacy. Therefore, we conducted population-based simulations to assess antiviral treatment effectiveness among different age groups based on its clinical efficacy.

METHODS

We collected COVID-19 data of Wuhan City from published literature and established a database (from 2 December 2019 to 16 March 2020). We developed an age-specific model to evaluate the effectiveness of antiviral treatment in patients with COVID-19. Efficacy was divided into three types: (1) viral activity reduction, reflected as transmission rate decrease [reduction was set as v (0-0.8) to simulate hypothetical antiviral treatments]; (2) reduction in the duration time from symptom onset to patient recovery/removal, reflected as a 1/γ decrease (reduction was set as 1-3 days to simulate hypothetical or real-life antiviral treatments, and the time of asymptomatic was reduced by the same proportion); (3) fatality rate reduction in severely ill patients (fc) [reduction (z) was set as 0.3 to simulate real-life antiviral treatments]. The population was divided into four age groups (groups 1, 2, 3 and 4), which included those aged ≤ 14; 15-44; 45-64; and ≥ 65 years, respectively. Evaluation indices were based on outbreak duration, cumulative number of cases, total attack rate (TAR), peak date, number of peak cases, and case fatality rate (f).

RESULTS

Comparing the simulation results of combination and single medication therapy s, all four age groups showed better results with combination medication. When 1/γ = 2 and v = 0.4, age group 2 had the highest TAR reduction rate (98.48%, 56.01-0.85%). When 1/γ = 2, z = 0.3, and v = 0.1, age group 1 had the highest reduction rate of f (83.08%, 0.71-0.12%).

CONCLUSIONS

Antiviral treatments are more effective in COVID-19 transmission control than in mortality reduction. Overall, antiviral treatments were more effective in younger age groups, while older age groups showed higher COVID-19 prevalence and mortality. Therefore, physicians should pay more attention to prevention of viral spread and patients deaths when providing antiviral treatments to patients of older age groups.