Authoritarian Crisis Response to COVID-19 in China

What explains the Chinese government's differentiated response to the COVID-19 pandemic? This chapter argues that the same sources of control in authoritarian crisis response that enable the state to mobilize resources and people hamper the flexibility and nimbleness needed to adapt amid uncertainty. It analyzes how political priorities in a predominantly top-down system and experience with past infectious disease outbreaks shape the public health approach to COVID-19 and examines the response from late 2019 through mid-2022 in three approximate phases: early missteps and institutional impediments, rapid shift in response effectiveness, and top-down control and cracks in zero-COVID. Initial reactions were dispersed and incremental as local officials wrestled with how loudly to sound the alarms on the emergence of a new respiratory virus that seemed to be spreading. Beijing eventually backed a centralized, coordinated effort. The ramped-up response was effective, if authoritarian and heavy-handed at times. Since then, the scale and speed of the state's ability to assemble testing, tracing, quarantining, and isolating capacity and other measures enabled China to generally enclose inevitable flare-ups in most of 2020 and 2021. But unyielding pursuit of dynamic zero-COVID policy through mid-2022 reveals a fragile flip side of dogged top-down control. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Optimal symptom combinations to aid COVID-19 case identification: Analysis from a community-based, prospective, observational cohort.

OBJECTIVES: Diagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health. METHODS: UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity. FINDINGS: UK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC. INTERPRETATION: We confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings.

Cancer and race: Two important riskfactors for COVID-19 incidence as captured by theCOVID Symptom Study real-time epidemiology tool

Background: The COVID-19 pandemic and response underscore the urgent need for real-time population-leveldata, especially for vulnerable populations (e.g., cancer patients, racial and ethnic minorities). Smartphoneapplications (apps) facilitate the collection of self-reported data at scale, the results of which can then be rapidlyredeployed to inform the public health response. The COVID Symptom Study is an app that was launched March24, 2020, and is now used by nearly 4 million people in the U.S., U.K., and Sweden. Methods: COVID Symptom Study app users self-report health status (e.g., symptoms, COVID-19 testing, healthcare utilization), comorbidities, demographics, and key risk factors for infection on a daily basis. Multivariableadjusted logistic regression models were used to determine the association of cancer and race with COVID-19prevalence, adjusting for age, sex, comorbidities, and risk factors for infection, from app launch through May 25,2020. Results: Among 23,266 individuals with cancer and 1,784,293 without cancer, we documented 155 and 10,249 self-reports of COVID-19, respectively. Compared to individuals without cancer, those with cancer had an increased riskof COVID-19 (adjusted odds ratio (aOR): 1.60;95% confidence interval (CI): 1.36-1.88). The association wasstronger among older participants >65 compared to younger participants (Pinteraction<0.001) and among males(aOR: 1.71;95%CI: 1.36-2.15) compared to females (aOR: 1.43;95%CI: 1.14-1.79;Pinteraction=0.02).Chemotherapy/immunotherapy was associated with a 2-fold increased risk of COVID-19 (aOR: 2.22;95% CI: 1.68-2.94) and risk of COVID-related hospitalization (aOR:2.47;95% CI: 2.22-2.76). In a separate analysis, wedocumented 8,990 self-reported cases of positive COVID-19 testing among 2,304,472 non-Hispanic whiteparticipants (93.6% of cohort);93 among 19,498 Hispanic participants;204 among 19,498 Black participants;608among 64,429 Asian participants;and 352 among 65,046 mixed race/other racial minorities. Compared with non-Hispanic white participants, the ORs for reporting a positive COVID-19 test for racial minorities ranged from 1.44(mixed race/other races) to 2.59 (Black). After accounting for risk factors for infection, comorbidities, andsociodemographic characteristics, the aORs were 1.37 (95% CI 1.09-1.72) for Hispanic participants, 1.42 (95% CI1.23-1.64) for Black participants, 1.44 (95% CI 1.33-1.57) for Asian participants, and 1.18 (95% CI 1.06-1.32) formixed race/other minorities. Conclusion: Our results demonstrate an increase in COVID-19 risk among ethnic minorities and individuals withcancer, particularly those on treatment with chemotherapy/immunotherapy. The association with minorities was notcompletely explained by other known risk factors for COVID-19 or sociodemographic characteristics. These findingshighlight the utility of app-based syndromic surveillance for quantifying the impact of the COVID-19 pandemic on at-risk populations.

Optimal symptom combinations to aid COVID-19 case identification: analysis from a community-based, prospective, observational cohort.

OBJECTIVES: Diagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health. METHODS: UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity. FINDINGS: UK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC. INTERPRETATION: We confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings.