The Negative Impact of COVID-19 on Life Insurers

To PCR or not? The impact of shifting policy from PCR to rapid antigen tests to diagnose COVID-19 during the omicron epidemic: a nationwide surveillance study

Background

Coronavirus disease 2019 (COVID-19) had caused huge impacts worldwide. Polymerase chain reaction (PCR) is the mainstay diagnostic modality. In most hospitals in Taiwan, samples for PCR are collected at emergency department (ER) or outdoor clinics to avoid virus spread inside hospitals. Home rapid antigen test (RAT) is a feasible, low-cost, and convenient tool with moderate sensitivity and high specificity, which can be performed at home to reduce hospital visits. Due to comparably low severity of omicron variant and high vaccine coverage (~80% residents fully vaccinated with AstraZeneca, Moderna, or Pfizer BioNTech COVID-19 vaccines as of March 2022), the policy was shifted from containment to co-existing with COVID-19 in Taiwan. Virus spread rapidly in the community after the ease of social restrictive measurements. To acquire a confirmed diagnosis, PCR testing was requested for people with suspected COVID-19 infection. As a consequence, people with respiratory symptoms or contact history surged into hospitals for PCR testing, thus, the medical capacity was challenged. The diagnostic policy was altered from PCR to RAT, but the impact of diagnostic policy change remains unclear.

Objectives

We conducted this study to investigate the number of COVID-19 cases, PCR testing, hospitalizations, mortalities, and hospital visits during the epidemic and evaluate the impact of diagnostic policy change on hospital visits.

Methods

The diagnostic policy change was implemented in late May 2022. We used nationwide and hospital-based data of COVID-19 cases, PCR testing, hospitalizations, mortalities, and hospital visits before and after policy change as of 31 Jul 2022.

Results

During the omicron epidemic, significant and synchronous increase of COVID-19 patients, PCR testing, hospital visits were observed. COVID-19 cases increased exponentially since April 2022 and the COVID-19 patients peaked in June (1,943, 55,571, and 61,511 average daily new cases in April, May, and June, respectively). The PCR testing peaked in May (85,788 daily tests) with high positive rate (81%). The policy of RAT as confirmatory diagnosis was implemented on 26 May 2022 and a substantial decline of PCR testing numbers occurred (85,788 and 83,113 daily tests in May and June). People hospitalized for COVID-19 peaked in June (821.8 patients per day) and decreased in July (549.5 patients). The mortality cases also peaked in June (147 cases/day). This trend was also validated by the hospital-based data with a significant decrease of emergency department visits (11,397 visits in May while 8,126 visits in June) and PCR testing (21,314 in May and 6,158 in June). The proportion of people purely for PCR testing also decreased (10-26 vs. 5-14%, before and after policy change, respectively).

Conclusions

The impact of diagnostic policy change was a complicated issue and our study demonstrated the huge impact of diagnostic policy on health seeking behavior. The PCR testing numbers and emergency department visits had substantial decrease after diagnostic policy change, and the plateau of epidemic peak eased gradually in ~1 month later. Widespread RAT application may contribute to the decreased hospital visits and preserve medical capacity. Our study provides some evidences for policy maker's reference.

Study on the Localization of Fangcang Shelter Hospitals During Pandemic Outbreaks

In the event of pandemic, it is essential for government authority to implement responses to control the pandemic and protect people's health with rapidity and efficicency. In this study, we first develop an evaluation framework consisting of the entropy weight method (EWM) and the technique for order preference by similarity to ideal solution (TOPSIS) to identify the preliminary selection of Fangcang shelter hospitals; next, we consider the timeliness of isolation and treatment of patients with different degrees of severity of the infectious disease, with the referral to and triage in Fangcang shelter hospitals characterized and two optimization models developed. The computational results of Model 1 and Model 2 are compared and analyzed. A case study in Xuzhou, Jiangsu Province, China, is used to demonstrate the real-life applicability of the proposed models. The two-stage localization method gives decision-makers more options in case of emergencies and can effectively designate the location. This article may give recommendations of and new insights into parameter settings in isolation hospital for governments and public health managers.

Application of Continuous Non-Gaussian Mortality Models with Markov Switchings to Forecast Mortality Rates

The ongoing pandemic has resulted in the development of models dealing with the rate of virus spread and the modelling of mortality rates μx,t. A new method of modelling the mortality rates μx,t with different time intervals of higher and lower dispersion has been proposed. The modelling was based on the Milevski–Promislov class of stochastic mortality models with Markov switches, in which excitations are modelled by second-order polynomials of results from a linear non-Gaussian filter. In contrast to literature models where switches are deterministic, the Markov switches are proposed in this approach, which seems to be a new idea. The obtained results confirm that in the time intervals with a higher dispersion of μx,t, the proposed method approximates the empirical data more accurately than the commonly used the Lee–Carter model.

Network Structure and Community Evolution Online: Behavioral and Emotional Changes in Response to COVID-19

Background: The measurement and identification of changes in the social structure in response to an exceptional event like COVID-19 can facilitate a more informed public response to the pandemic and provide fundamental insights on how collective social processes respond to extreme events. Objective: In this study, we built a generalized framework for applying social media data to understand public behavioral and emotional changes in response to COVID-19. Methods: Utilizing a complete dataset of Sina Weibo posts published by users in Wuhan from December 2019 to March 2020, we constructed a time-varying social network of 3.5 million users. In combination with community detection, text analysis, and sentiment analysis, we comprehensively analyzed the evolution of the social network structure, as well as the behavioral and emotional changes across four main stages of Wuhan's experience with the epidemic. Results: The empirical results indicate that almost all network indicators related to the network's size and the frequency of social interactions increased during the outbreak. The number of unique recipients, average degree, and transitivity increased by 24, 23, and 19% during the severe stage than before the outbreak, respectively. Additionally, the similarity of topics discussed on Weibo increased during the local peak of the epidemic. Most people began discussing the epidemic instead of the more varied cultural topics that dominated early conversations. The number of communities focused on COVID-19 increased by nearly 40 percent of the total number of communities. Finally, we find a statistically significant "rebound effect" by exploring the emotional content of the users' posts through paired sample t-test (P = 0.003). Conclusions: Following the evolution of the network and community structure can explain how collective social processes changed during the pandemic. These results can provide data-driven insights into the development of public attention during extreme events.