Characterizing pandemic waves: A latent class analysis of COVID-19 spread across US counties

Self-Reported Long COVID and Its Impact on COVID-19-Related Worries and Behaviors After Lifting the COVID-19 Restrictions in China

OBJECTIVE

Since the lifting of the COVID-19 restrictions in China in November 2022, there has been a notable surge in the COVID-19 infection rate. Little is known about the prevalence of long COVID among the general adult population and its impact on COVID-19-related worries and behaviors after the policy change.

METHODS

This cross-sectional study recruited 1530 adults with prior COVID-19 infection in Guangzhou from February to March 2023. Logistic regression analyses and trend analyses were performed to investigate the associations between long COVID- and COVID-19-related worries and preventive behaviors.

RESULTS

The estimated prevalence of long COVID among adults in China was 18.0% (95% confidence interval: 16.1% to 20.0%). Common long COVID symptoms included cough (60.7%), fatigue (47.6%), dyspnea (34.5%), palpitation (26.2%), and insomnia (25.1%). Adjusted for background variables, individuals with long COVID exhibited higher level of COVID-19-related worries compared to those who had fully recovered from the infection (reference: without long COVID; adjusted odds ratios ranged from 1.87 to 2.55, all p values < 0.001). Participants primarily expressed worries regarding the potential for COVID-19 reinfection, the impact of the pandemic on daily life, the increasing number of COVID-19 cases and deaths, and the capacity of the healthcare system. While long COVID did not statistically significantly affect their preventive behaviors.

CONCLUSIONS

Long COVID was prevalent among the general adult population in China after lifting the COVID-19 restrictions, and it had a significant impact on COVID-19-related worries. This study highlights the importance of monitoring the mental health of individuals with long COVID and developing targeted intervention strategies to improve their adherence to preventive measures.

Analyzing the Trends of COVID-19 and Human Activity Intensity in Malaysia

COVID-19 has struck the world with multiple waves. Each wave was caused by a variant and presented different peaks and baselines. This made the identification of waves with the time series of the cases a difficult task. Human activity intensities may affect the occurrence of an outbreak. We demonstrated a metric of time series, namely log-moving-average-ratio (LMAR), to identify the waves and directions of the changes in the disease cases and check-ins (MySejahtera). Based on the detected waves and changes, we explore the relationship between the two. Using the stimulus-organism-response model with our results, we presented a four-stage model: (1) government-imposed movement restrictions, (2) revenge travel, (3) self-imposed movement reduction, and (4) the new normal. The inverse patterns between check-ins and pandemic waves suggested that the self-imposed movement reduction would naturally happen and would be sufficient for a smaller epidemic wave. People may spontaneously be aware of the severity of epidemic situations and take appropriate disease prevention measures to reduce the risks of exposure and infection. In summary, LMAR is more sensitive to the waves and could be adopted to characterize the association between travel willingness and confirmed disease cases.