COVID-19 seeding time and doubling time model: an early epidemic risk assessment tool

COVID-19 Pandemic Risk Assessment: Systematic Review

Background

The COVID-19 pandemic presents the possibility of future large-scale infectious disease outbreaks. In response, we conducted a systematic review of COVID-19 pandemic risk assessment to provide insights into countries' pandemic surveillance and preparedness for potential pandemic events in the post-COVID-19 era.

Objective

We aim to systematically identify relevant articles and synthesize pandemic risk assessment findings to facilitate government officials and public health experts in crisis planning.

Methods

This study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and included over 620,000 records from the World Health Organization COVID-19 Research Database. Articles related to pandemic risk assessment were identified based on a set of inclusion and exclusion criteria. Relevant articles were characterized based on study location, variable types, data-visualization techniques, research objectives, and methodologies. Findings were presented using tables and charts.

Results

Sixty-two articles satisfying both the inclusion and exclusion criteria were identified. Among the articles, 32.3% focused on local areas, while another 32.3% had a global coverage. Epidemic data were the most commonly used variables (74.2% of articles), with over half of them (51.6%) employing two or more variable types. The research objectives covered various aspects of the COVID-19 pandemic, with risk exposure assessment and identification of risk factors being the most common theme (35.5%). No dominant research methodology for risk assessment emerged from these articles.

Conclusion

Our synthesized findings support proactive planning and development of prevention and control measures in anticipation of future public health threats.

A review of strategies and their effectiveness in reducing indoor airborne transmission and improving indoor air quality

Airborne transmission arises through the inhalation of aerosol droplets exhaled by an infected person and is now thought to be the primary transmission route of COVID-19. Thus, maintaining adequate indoor air quality levels is vital in mitigating the spread of the airborne virus. The cause-and-effect flow of various agents involved in airborne transmission of viruses has been investigated through a systematic literature review. It has been identified that the airborne virus can stay infectious in the air for hours, and pollutants such as particulate matter (PM10, PM2.5), Nitrogen dioxide (NO2), Sulphur dioxide (SO2), Carbon monoxide (CO), Ozone (O3), Carbon dioxide (CO2), and Total Volatile Organic Compounds (TVOCs) and other air pollutants can enhance the incidence, spread and mortality rates of viral disease. Also, environmental quality parameters such as humidity and temperature have shown considerable influence in virus transmission in indoor spaces. The measures adopted in different research studies that can curb airborne transmission of viruses for an improved Indoor Air Quality (IAQ) have been collated for their effectiveness and limitations. A diverse set of building strategies, components, and operation techniques from the recent literature pertaining to the ongoing spread of COVID-19 disease has been systematically presented to understand the current state of techniques and building systems that can minimize the viral spread in built spaces This comprehensive review will help architects, builders, realtors, and other organizations improve or design a resilient building system to deal with COVID-19 or any such pandemic in the future.

The Impact of COVID-19 Pandemic on Halting Sustainable Development in the Colca y Volcanes de Andagua UNESCO Global Geopark in Peru—Prospects and Future

Events, such as the COVID-19 pandemic, that rapidly impact global communication and travel have significant consequences for the tourism industry, which is one of the pillars of global development. We assess the impacts of the COVID-19 crisis on the Colca y Volcanes de Andagua UNESCO Global Geopark in Peru. The Colca y Volcanes de Andagua Geopark was established immediately prior to the pandemic in October 2019. The instability of the government in Peru during the pandemic and the difficult living conditions in the high Andes, such as the lack of drinking water, cleaning agents, medical care, and the high levels of poverty, particularly in the geopark region, has contributed to the significantly high COVID-19 infection rates. In addition, detrimental impacts faced by the local community are a direct result of a reduction in travellers to the area due to legislative restrictions, which have had negative consequences on the local tourism industry. There is an urgent need for the recovery of the local tourism industry to prevent the permanent closure of tourism facilities and to minimise poverty rates.

A novel analysis approach to determining the case fatality rate of COVID-19 in Italy

BACKGROUND

The Coronavirus Disease 2019 (COVID-19), which emerged in December 2019, has spread rapidly around the world and has become a serious public health event endangering human life. With regard to COVID-19, there are still many unknowns, such as the exact case fatality rate.

OBJECTIVE

The main objective of this study was to explore the value of the discharged case fatality rate (DCFR) to make more accurate forecasts of epidemic trends the of COVID-19 in Italy.

METHODS

We retrieved the epidemiological data of COVID-19 in Italy published by the John Hopkins Coronavirus Resource Center. We then used the proportion of daily deaths and total deaths to calculate the discharged case fatality rate (tDCFR), monthly discharged case fatality rate (mDCFR), and stage discharged case fatality rate (sDCFR). Furthermore, we analyzed the trend in mDCFR between January and December 2020 using Joinpoint Regression Analysis and used the ArcGIS version 10.7 software to visualize the spatial distribution of epidemic case fatality rate and assigned different colors to each province based on the CFR or tDCFR.

RESULTS

We calculate the number and obtain the new index tDCFR and mDCFR for calculating the fatality rate. The results show that the overall trend of tDCFR and mDCFR fluctuates greatly from January to May. After reaching the peak, it first rises rapidly, then falls rapidly, and finally stabilizes. The map shows that the provinces with high tDCFR were Emilia-Romagna, Puglia and Lombardia. The change trend of mDCFR over time was divided into two stages, the first stage (from January to May) and the second stage (from June to December). Among the six selected countries, the United States has the highest tDCFR (4.26%), while the tDCFR of the remaining countries is between 0.98% and 2.72%.

CONCLUSIONS

We provide a new perspective for assessing the mortality of COVID-19 in Italy,which can use these ever-changing data to calculate a more accurate case fatality rate and scientifically predict the development trend of the epidemic.

CLINICALTRIAL

Are COVID-19 models blind to the social determinants of health? A systematic review protocol

INTRODUCTION

Infectious disease models are important tools to inform public health policy decisions. These models are primarily based on an average population approach and often ignore the role of social determinants in predicting the course of a pandemic and the impact of policy interventions. Ignoring social determinants in models may cause or exacerbate inequalities. This limitation has not been previously explored in the context of the current pandemic, where COVID-19 has been found to disproportionately affect marginalised racial, ethnic and socioeconomic groups. Therefore, our primary goal is to identify the extent to which COVID-19 models incorporate the social determinants of health in predicting outcomes of the pandemic.

METHODS AND ANALYSIS

We will search MEDLINE, EMBASE, Cochrane Library and Web of Science databases from December 2019 to August 2020. We will assess all infectious disease modelling studies for inclusion of social factors that meet the following criteria: (a) focused on human spread of SARS-CoV-2; (b) modelling studies; (c) interventional or non-interventional studies; and (d) focused on one of the following outcomes: COVID-19-related outcomes (eg, cases, deaths), non-COVID-19-related outcomes (ie, impacts of the pandemic or control policies on other health conditions or health services), or impact of the pandemic or control policies on economic outcomes. Data will only be extracted from models incorporating social factors. We will report the percentage of models that considered social factors, indicate which social factors were considered, and describe how social factors were incorporated into the conceptualisation and implementation of the infectious disease models. The extracted data will also be used to create a narrative synthesis of the results.

ETHICS AND DISSEMINATION

Ethics approval is not required as only secondary data will be collected. The results of this systematic review will be disseminated through peer-reviewed publication and conference proceedings.

PROSPERO REGISTRATION NUMBER

CRD42020207706.

Preliminary Assessment of Chinese Strategy in Controlling Reemergent Local Outbreak of COVID-19

Reemergent local outbreaks of coronavirus disease 2019 (COVID-19) have occurred in China, yet few Chinese response strategies and its evaluation have been reported. This study presents a preliminary assessment of Chinese strategy in controlling reemergent local outbreaks of COVID-19. Time course of accumulative and daily new cases and time-varying reproductive numbers (Rt) of outbreak areas were presented. The asymptomatic rate, days required to control the outbreaks, seeding time (ST), and doubling time (DT) of areas with over 96 reemergent cases were calculated. National and local year-on-year growth rates of gross domestic product (GDP) were presented. Accumulative numbers of 30, 8, 11, 430, 15, 139, 1,067, 382, 42, and 94 confirmed reemergent COVID-19 cases were diagnosed in Hulun Buir, Shanghai, Tianjin, Kashgar, Qingdao, Dalian, Urumchi, Beijing, Jilin, and Harbin, respectively. Among them, maximum rate of asymptomatic infections was 81.9%. Time required to control the local outbreaks in the areas given above varied from 29 to 51 days. After activation of outbreak responses, the late-stage DTs of Kashgar, Urumchi, Beijing, and Dalian were apparently lengthened compared to the early-stage DTs. Although the year-on-year GDP growth rate of Urumchi was slightly affected, the GDP growth rate of Dalian, Beijing, Jilin, and Harbin kept rising during the reemergence. Moreover, the year-on-year GDP growth rate of Mainland China turned positive regardless of the reemergent local outbreaks. In general, the Chinese strategy to maintain the status of no or minimal transmission was effective in balancing the control of COVID-19 reemergent local outbreak and the recovery of economy.

Impact of Nonpharmacological Public Health Interventions on Epidemiological Parameters of COVID-19 Pandemic in India

Background Public health interventions are epidemiologically sound and cost-effective methods to control disease burden. Non-pharmacological public health interventions are the only mode to control diseases in the absence of medication. Objective To find the impact of public health interventions on the epidemiological indicators of disease progression. Methods This is a secondary data analysis done on COVID-19 data. The median doubling time and R0 were calculated for a rolling period of seven days. Interventions were scored from zero to three with an increasing level of stringency. Multivariate linear regression was performed to find the role of individual interventions on R0 and the median doubling time. Results The highest intervention score was reported in the lockdown phase, which gradually decreased to the lowest level of 22. The R0 values settled to a level of 1.25, and the median doubling time increased to 20 days at the end of the study. Public awareness and public health laws were found to be related to both R0 and the median doubling time in the pre-lockdown phase only. Conclusion The implementation of interventions at the ground level is one of the key factors in the success of public health interventions. Post implementation, poor effectiveness of many interventions is evident from the study. Further, studies related to the sequence of interventions are required to further analyze the poor effect of the interventions.

Optimizing time-limited non-pharmaceutical interventions for COVID-19 outbreak control

Retrospective analyses of the non-pharmaceutical interventions (NPIs) used to combat the ongoing COVID-19 outbreak have highlighted the potential of optimizing interventions. These optimal interventions allow policymakers to manage NPIs to minimize the epidemiological and human health impacts of both COVID-19 and the intervention itself. Here, we use a susceptible-infectious-recovered (SIR) mathematical model to explore the feasibility of optimizing the duration, magnitude and trigger point of five different NPI scenarios to minimize the peak prevalence or the attack rate of a simulated UK COVID-19 outbreak. An optimal parameter space to minimize the peak prevalence or the attack rate was identified for each intervention scenario, with each scenario differing with regard to how reductions to transmission were modelled. However, we show that these optimal interventions are fragile, sensitive to epidemiological uncertainty and prone to implementation error. We highlight the use of robust, but suboptimal interventions as an alternative, with these interventions capable of mitigating the peak prevalence or the attack rate over a broader, more achievable parameter space, but being less efficacious than theoretically optimal interventions. This work provides an illustrative example of the concept of intervention optimization across a range of different NPI strategies. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Superspreading of SARS-CoV-2 in the USA

A number of epidemics, including the SARS-CoV-1 epidemic of 2002-2004, have been known to exhibit superspreading, in which a small fraction of infected individuals is responsible for the majority of new infections. The existence of superspreading implies a fat-tailed distribution of infectiousness (new secondary infections caused per day) among different individuals. Here, we present a simple method to estimate the variation in infectiousness by examining the variation in early-time growth rates of new cases among different subpopulations. We use this method to estimate the mean and variance in the infectiousness, β, for SARS-CoV-2 transmission during the early stages of the pandemic within the United States. We find that σβ/μβ ≳ 3.2, where μβ is the mean infectiousness and σβ its standard deviation, which implies pervasive superspreading. This result allows us to estimate that in the early stages of the pandemic in the USA, over 81% of new cases were a result of the top 10% of most infectious individuals.

Meteorological factors, governmental responses and COVID-19: Evidence from four European countries

With the global lockdown, meteorological factors are highly discussed for COVID-19 transmission. In this study, national-specific and region-specific data sets from Germany, Italy, Spain and the United Kingdom were used to explore the effect of temperature, absolute humidity and diurnal temperature range (DTR) on COVID-19 transmission. From February 1st to November 1st, a 7-day COVID-19 case doubling time (Td), meteorological factors with cumulative 14-day-lagged, government response index and other factors were fitted in the distributed lag nonlinear models. The overall relative risk (RR) of the 10th and the 25th percentiles temperature compared to the median were 0.0074 (95% CI: 0.0023, 0.0237) and 0.1220 (95% CI: 0.0667, 0.2232), respectively. The pooled RR of lower (10th, 25th) and extremely high (90th) absolute humidity were 0.3266 (95% CI: 0.1379, 0.7734), 0.6018 (95% CI: 0.4693, 0.7718) and 0.3438 (95% CI: 0.2254, 0.5242), respectively. While the DTR did not have a significant effect on Td. The total cumulative effect of temperature (10th) and absolute humidity (10th, 90th) on Td increased with the change of lag days. Similarly, a decline in temperature and absolute humidity at cumulative 14-day-lagged corresponded to the lower RR on Td in pooled region-specific effects. In summary, the government responses are important factors in alleviating the spread of COVID-19. After controlling that, our results indicate that both the cold and the dry environment also likely facilitate the COVID-19 transmission.

Can particulate matter be identified as the primary cause of the rapid spread of CoViD-19 in some areas of Northern Italy?

Northern Italy was the most affected by CoViD-19 compared to other Italian areas and comprises zones where air pollutants concentration was higher than in the rest of Italy. The aim of the research is to determine if particulate matter (PM) has been the primary cause of the high CoViD-19 spread rapidity in some areas of Northern Italy. Data of PM for all the 41 studied cities were collected from the local environmental protection agencies. To compare air quality data with epidemiological data, a statistical analysis was conducted identifying the correlation matrices of Pearson and Spearman, considering also the possible incubation period of the disease. Moreover, a model for the evaluation of the epidemic risk, already proposed in literature, was used to evaluate a possible influence of PM on CoViD-19 spread rapidity. The results exclude that PM alone was the primary cause of the high CoVid-19 spread rapidity in some areas of Northern Italy. Further developments are necessary for a better comprehension of the influence of atmospheric pollution parameters on the rapidity of spread of the virus SARS-CoV-2, since a synergistic action with other factors (such as meteorological, socio-economic and cultural factors) could not be excluded by the present study.