Impact of Modelling Approach on the Estimation of Government Intervention Effects in COVID-19

The efficacy of government interventions in epidemic has become a hot subject since the onset of COVID-19. There is however much variation in the results quantifying the effects of interventions, which is partly related to the varying modelling approaches employed by existing studies. This paper therefore aims to examine how the choice of modelling approach would affect the estimation results of intervention effects, by experimenting with different modelling approaches on a same data set composed of the 500 most affected U.S. counties. We compare the most frequently used methods from the two classes of modelling approaches, which are Bayesian hierarchical model from the class of computational approach and difference-in-difference from the class of natural experimental approach. We find that computational methods are likely to produce larger estimates of intervention effects due to simultaneous voluntary behavioral changes. In contrast, natural experimental methods are more likely to extract the true effect of interventions. Among different difference-in-difference estimators, the two-way fixed effect estimator seems to be an efficient one. Our work can inform the methodological choice of future research on this topic, as well as more robust re-interpretation of existing works, to facilitate both future epidemic response plans and the science of public health.

Estimating the transmissibility of SARS-CoV-2 during periods of high, low and zero case incidence

Against a backdrop of widespread global transmission, a number of countries have successfully brought large outbreaks of COVID-19 under control and maintained near-elimination status. A key element of epidemic response is the tracking of disease transmissibility in near real-time. During major outbreaks, the reproduction rate can be estimated from a time-series of case, hospitalisation or death counts. In low or zero incidence settings, knowing the potential for the virus to spread is a response priority. Absence of case data means that this potential cannot be estimated directly. We present a semi-mechanistic modelling framework that draws on time-series of both behavioural data and case data (when disease activity is present) to estimate the transmissibility of SARS-CoV-2 from periods of high to low -- or zero -- case incidence, with a coherent transition in interpretation across the changing epidemiological situations. Of note, during periods of epidemic activity, our analysis recovers the effective reproduction number, while during periods of low -- or zero -- case incidence, it provides an estimate of transmission risk. This enables tracking and planning of progress towards the control of large outbreaks, maintenance of virus suppression, and monitoring the risk posed by re-introduction of the virus. We demonstrate the value of our methods by reporting on their use throughout 2020 in Australia, where they have become a central component of the national COVID-19 response.

Development of the COVID-19 Real-Time Information System for Preparedness and Epidemic Response (CRISPER), Australia

Accurate and current information has been highlighted across the globe as a critical requirement for the COVID-19 pandemic response. To address this need, many interactive dashboards providing a range of different information about COVID-19 have been developed. A similar tool in Australia containing current information about COVID-19 could assist general practitioners and public health responders in their pandemic response efforts. The COVID-19 Real-time Information System for Preparedness and Epidemic Response (CRISPER) has been developed to provide accurate and spatially explicit real-time information for COVID-19 cases, deaths, testing and contact tracing locations in Australia. Developed based on feedback from key users and stakeholders, the system comprises three main components: (1) a data engine; (2) data visualization and interactive mapping tools; and (3) an automated alert system. This system provides integrated data from multiple sources in one platform which optimizes information sharing with public health responders, primary health care practitioners and the general public.

Globalization and the Burden of Disease

More equitable and need-oriented funding of health services and research would safeguard everyone’s health. Worldwide health expenditures on health disproportionately address problems of the well-off, while research on diseases like malaria affecting hundreds of millions of mostly low-income people are underfunded. Nor are sufficient resources devoted to mental illness, traffic injuries, and natural disasters. As people in low-income countries live longer, chronic, non-communicable, and lifestyle diseases add to long-standing burdens of infectious and parasitic disease, and maternal and child health. This epidemiological transition calls for universal access to health services, which will also improve these countries’ ability to detect and respond to infectious diseases like the COVID-19 pandemic. The World Health Organization, coordinator of global epidemic response, needs to be freed from its downward spiral of decreased effectiveness, frozen funding, and increased politicization. Statistics on global causes of death and disability elevate the importance of social determinants of health.

Addressing hospitalisations with non-error-free data by generalised SEIR modelling of COVID-19 pandemic

Successive generalisations of the basic SEIR model have been proposed to accommodate the different needs of the organisations handling the SARS-CoV-2 epidemic. These generalisations have not been able until today to represent the potential of the epidemic to overwhelm hospital capacity until today. This work builds on previous generalisations, including a new compartment for hospital occupancy that allows accounting for the infected patients that need specialised medical attention. Consequently, a deeper understanding of the hospitalisations rate and probability as well as of the recovery rates for hospitalised and non-hospitalised individuals is achieved, offering new information and predictions of crucial importance for the planning of the health systems and global epidemic response. Additionally, a new methodology to calibrate epidemic flows between compartments is proposed. We conclude that the two-step calibration procedure is able to recalibrate non-error-free data and showed crucial to reconstruct the series in a specific situation characterised by significant errors over the official recovery cases. The performed modelling also allowed us to understand how effective the several interventions (lockdown or other mobility restriction measures) were, offering insight for helping public authorities to set the timing and intensity of the measures in order to avoid the implosion of the health systems.

Psychometric Analysis and Coupling of Emotions Between State Bulletins and Twitter in India During COVID-19 Infodemic

COVID-19 infodemic has been spreading faster than the pandemic itself. The misinformation riding upon the infodemic wave poses a major threat to people’s health and governance systems. Managing this infodemic not only requires mitigating misinformation but also an early understanding of underlying psychological patterns. In this study, we present a novel epidemic response management strategy. We analyze the psychometric impact and coupling of COVID-19 infodemic with official COVID-19 bulletins at the national and state level in India. We looked at them from the psycholinguistic lens of emotions and quantified the extent and coupling between them. We modified Empath, a deep skipgram-based lexicon builder, for effective capture of health-related emotions. Using this, we analyzed the lead-lag relationships between the time-evolution of these emotions in social media and official bulletins using Granger’s causality. It showed that state bulletins led the social media for some emotions such as Medical Emergency. In contrast, social media led the government bulletins for some topics such as hygiene, government, fun, and leisure. Further insights potentially relevant for policymakers and communicators engaged in mitigating misinformation are also discussed. We also introduce CoronaIndiaDataset, the first social-media-based Indian COVID-19 dataset at the national and state levels with over 5.6 million national and 2.6 million state-level tweets for the first wave of COVID-19 in India and 1.2 million national tweets for the second wave of COVID-19 in India.

Epidemic Response: Follow-up and lessons learned from the US response to the evolving and conflicting risk information, focusing on education and clinical environments

In this follow-up panel, we discuss what we have learned over the last year about responding to an epidemic or pandemic that has demonstrated a level of transmission unprecedented in the modern era. Two medical doctors that have worked on the front of this pandemic share their experiences transitioning from the “sharp end” of the response. Decisions about how to mitigate hazards have occurred at the personal, institutional, and health policy levels, in real-time, with frequent adaptation, and often in advance of concrete evidence. Over the course of the pandemic, hospital systems revised existing protocols to manage perceived risks in real time using emerging information from other centers. With the introduction of vaccines, there is a new type of risk perception. Is the vaccine perceived to be safe? Is there a disparity in perception among different population groups? That said, analyses are also complicated by emerging viral mutations with unclear implications. What factors increase or decrease public compliance with precautions? How are US education policymakers deciding about face-to-face classroom instruction? This panel includes a warnings expert, an expert on education policy, and two practicing physicians.

Spatial scales, patterns, and positivity trends of SARS-CoV-2 pandemics in mass rapid antigen testing in Slovakia

We study geographical epidemic scales and patterns and positivity trends of SARS-CoV-2 pandemics in mass antigen testing in Slovakia in 2020. The observed test positivity was exponentially distributed with a long scale exponential spatial trend, and its characteristic correlation length was approximately 10 km. Spatial scales also play an important role in test positivity reduction between two consecutive testing rounds. While test positivity decreased in all counties, it increased in individual municipalities with low test positivity in the earlier testing round in a way statistically different from a mean-reversion process. Also, non-residents testing influences the mass testing results as test positivity of non-residents was higher than of residents when testing was offered only in municipalities with the highest positivity in previous rounds. Our results provide direct guidance for pandemic geographical data surveillance and epidemic response management.

The COVID-19 pandemic in Africa’s Island Nations: A descriptive study of the course of the COVID-19 outbreaks and the national response plans

Introduction: The main objective is to present an overview of the evolution of the COVID-19pandemic in the six African island nations: Cabo Verde, Comoros, Madagascar, Mauritius, SãoTomé e Príncipe and Seychelles, up until 29 November 2020. The relevance of studying theoutbreak in these countries is their distinct geography, which may facilitate rapid closure andcontrol of their international borders. Here, we investigate whether this geography may haveled to an effective response and management of their respective COVID-19 epidemics.Methods: A literature review and analysis of national public health reports, officialcoronavirus websites and previously published research in each of the studied countries fromthe start of the pandemic through 29 November 2020 was performed. Data on metrics on thecountry-specific progression of COVID-19, the level of strictness of the governmental policies,the testing practices, as well the national healthcare systems, the description and the state ofhealth of the populations in the African island nations were reported.Results: Five out of six countries controlled their respective COVID-19 epidemics at an earlystage in the context of the total number of confirmed cases and deaths. In Cabo Verde, therewas an increasing number of cases as of 29 November 2020, when 10,526 total cases and 104total deaths were reported nationally. All six nations maintained a case fatality rate (CFR)lower than the global average, estimated between 2 - 3% in previously published research.Among the island nations, Mauritius had the highest CFR of 2%.Discussion: African island nations have different demographic, socioeconomic, and healthcareprofiles. However, their shared geographic characteristics likely played a role in limiting thespread of the infection. Furthermore, data from these nations support the idea that theimplementation of strict restrictions at an early stage, such as border closure and lockdowns,was crucial for the epidemic response.