The descriptive epidemiology of coronavirus disease 2019 during the epidemic period in Lu'an, China: achieving limited community transmission using proactive response strategies

The role and contribution of civil society and community actors in COVID-19 prevention and control: the case of the COMVID COVID-19 movement in Burkina Faso

Communities should play a crucial role in the fight against public health emergencies but ensuring their effective and sustained engagement remains a challenge in many countries. In this article, we describe the process of mobilising community actors to contribute to the fight against COVID-19 in Burkina Faso. During the early days of the pandemic, the national COVID-19 response plan called for the involvement of community actors, but no strategy had been defined for this purpose. The initiative to involve community actors in the fight against COVID-19 was taken, independently of the government, by 23 civil society organisations gathered through a platform called 'Health Democracy and Citizen Involvement (DES-ICI)'. In April 2020, this platform launched the movement 'Communities are committed to Eradicate COVID-19 (COMVID COVID-19)' which mobilised community-based associations organised into 54 citizen health watch units (CCVS) in Ouagadougou city. These CCVS worked as volunteers, performing door-to-door awareness campaigns. The psychosis created by the pandemic, the proximity of civil society organisations to the communities and the involvement of religious, customary and civil authorities facilitated the expansion of the movement. Given the innovative and promising nature of these initiatives, the movement gained recognition that earned them a seat on the national COVID-19 response plan. This gave them credibility in the eyes of the national and international donors, thus facilitating the mobilisation of resources for the continuity of their activities. However, the decrease in financial resources to offset the community mobilisers gradually reduced the enthusiasm for the movement. In a nutshell, the COMVID COVID-19 movement fostered dialogues and collaboration among civil society, community actors and the Ministry of Health, which plans to engage the CCVS beyond the COVID-19 response, for the implementation of other actions within the national community health policy.

Asymptomatic SARS-CoV-2 infection: A systematic review and meta-analysis

Quantification of asymptomatic infections is fundamental for effective public health responses to the COVID-19 pandemic. Discrepancies regarding the extent of asymptomaticity have arisen from inconsistent terminology as well as conflation of index and secondary cases which biases toward lower asymptomaticity. We searched PubMed, Embase, Web of Science, and World Health Organization Global Research Database on COVID-19 between January 1, 2020 and April 2, 2021 to identify studies that reported silent infections at the time of testing, whether presymptomatic or asymptomatic. Index cases were removed to minimize representational bias that would result in overestimation of symptomaticity. By analyzing over 350 studies, we estimate that the percentage of infections that never developed clinical symptoms, and thus were truly asymptomatic, was 35.1% (95% CI: 30.7 to 39.9%). At the time of testing, 42.8% (95% prediction interval: 5.2 to 91.1%) of cases exhibited no symptoms, a group comprising both asymptomatic and presymptomatic infections. Asymptomaticity was significantly lower among the elderly, at 19.7% (95% CI: 12.7 to 29.4%) compared with children at 46.7% (95% CI: 32.0 to 62.0%). We also found that cases with comorbidities had significantly lower asymptomaticity compared to cases with no underlying medical conditions. Without proactive policies to detect asymptomatic infections, such as rapid contact tracing, prolonged efforts for pandemic control may be needed even in the presence of vaccination.

Clinical Features of COVID-19 Patients in Jordan: A Study of 508 Patients

BACKGROUND

The symptoms of COVID-19 have a wide range of severity ranging from no symptoms at all to mild symptoms, such as fever, cough, sore throat, general weakness. Moreover, in some situations, patients may develop severe complications as pneumonia, and sepsis, leading to death. This study aims to investigate the characteristic features of COVID-19 patients based on their medical condition prior to COVID-19 diagnosis.

METHODS

A retrospective cohort study took place between the 1st of April 2020 and the 31st of June 2020 in Prince Hamzah Hospital, Jordan. Patients were diagnosed by the Real-Time Reverse Transcriptase (RT)-PCR Diagnostic Panel, either through screening or for those who developed symptoms. During this period, patients who tested positive for COVID 19 were admitted to the hospital regardless of their symptoms according to the local government health policies. A total of 508 Patients were involved and divided into two groups based on the presence or absence of chronic illnesses prior to COVID-19 diagnosis.

RESULTS

A total of 371 patients were medically free (220 males and 151 females). Among them, 153 patients were symptomatic (41.2%), with an average hospitalization of 18 days. Generalized malaise, dry cough, and fever were the most common reported symptoms (51%, 45.8%, and 41.8%, respectively). On the other hand, the total number of COVID-19 patients with predefined comorbidities was 137 (93 males and 44 females). Among them, 86 patients (62.8%) were symptomatic, with an average duration of admission of 19.3 days. Similar to medically free patients, dry cough, generalized malaise, and fever were the most commonly reported symptoms (50%, 43%, and 38.4%, respectively). There was a statistically significant correlation between the presence of chronic illnesses and the development of symptoms among COVID-19 patients (P = 0.0001).

CONCLUSION

Dry cough, generalized malaise, and fever were the most commonly reported symptoms among our patients regardless of their medical condition. The average duration of hospitalization in medically free patients was less than patients with comorbidities, and it was less among asymptomatic compared to symptomatic patients. More than half of our COVID-19 patients were male and asymptomatic. A significant correlation between patients' medical condition and the possibility of developing symptoms in response to COVID-19 was identified.

Serial intervals and case isolation delays for COVID-19: a systematic review and meta-analysis

Background

Estimates of the serial interval distribution contribute to our understanding of the transmission dynamics of coronavirus disease 2019 (COVID-19). Here, we aimed to summarize the existing evidence on serial interval distributions and delays in case isolation for COVID-19.

Methods

We conducted a systematic review of the published literature and preprints in PubMed on two epidemiological parameters namely serial intervals and delay intervals relating to isolation of cases for COVID-19 until 22 October, 2020 following predefined eligibility criteria. We assessed the variation in these parameter estimates by correlation and regression analysis.

Results

Of 103 unique studies identified on serial intervals of COVID-19, 56 were included providing 129 estimates and of 451 unique studies on isolation delays, 18 studies were included providing 74 estimates. Serial interval estimates varied from 1.0 to 9.9 days, while case isolation delays varied from 1.0 to 12.5 days which were associated with spatial, methodological and temporal factors. In mainland China, the pooled mean serial interval was 6.2 (range, 5.1-7.8) days before the epidemic peak and reduced to 4.9 (range, 1.9-6.5) days after the epidemic peak. Similarly, the pooled mean isolation delay related intervals were 6.0 (range, 2.9-12.5) days and 2.4 (range, 2.0-2.7) days before and after the epidemic peak, respectively. There was a positive association between serial interval and case isolation delay.

Conclusions

Temporal factors, such as different control measures and case isolation in particular led to shorter serial interval estimates over time. Correcting transmissibility estimates for these time-varying distributions could aid mitigation efforts.

Rapid review of available evidence on the serial interval and generation time of COVID-19

The serial interval is the time between symptom onsets in an infector-infectee pair. The generation time, also known as the generation interval, is the time between infection events in an infector-infectee pair. The serial interval and the generation time are key parameters for assessing the dynamics of a disease. A number of scientific papers reported information pertaining to the serial interval and/or generation time for COVID-19.

OBJECTIVE

Conduct a review of available evidence to advise on appropriate parameter values for serial interval and generation time in national COVID-19 transmission models for Ireland and on methodological issues relating to those parameters.

METHODS

We conducted a rapid review of the literature covering the period 1 January 2020 and 21 August 2020, following predefined eligibility criteria. Forty scientific papers met our inclusion criteria and were included in the review.

RESULTS

The mean of the serial interval ranged from 3.03 to 7.6 days, based on 38 estimates, and the median from 1.0 to 6.0 days (based on 15 estimates). Only three estimates were provided for the mean of the generation time. These ranged from 3.95 to 5.20 days. One estimate of 5.0 days was provided for the median of the generation time.

DISCUSSION

Estimates of the serial interval and the generation time are very dependent on the specific factors that apply at the time that the data are collected, including the level of social contact. Consequently, the estimates may not be entirely relevant to other environments. Therefore, local estimates should be obtained as soon as possible. Careful consideration should be given to the methodology that is used. Real-time estimations of the serial interval/generation time, allowing for variations over time, may provide more accurate estimates of reproduction numbers than using conventionally fixed serial interval/generation time distributions.

Rapid review of available evidence on the serial interval and generation time of COVID-19

The serial interval is the time between symptom onsets in an infector-infectee pair. The generation time, also known as the generation interval, is the time between infection events in an infector-infectee pair. The serial interval and the generation time are key parameters for assessing the dynamics of a disease. A number of scientific papers reported information pertaining to the serial interval and/or generation time for COVID-19.

OBJECTIVE

Conduct a review of available evidence to advise on appropriate parameter values for serial interval and generation time in national COVID-19 transmission models for Ireland and on methodological issues relating to those parameters.

METHODS

We conducted a rapid review of the literature covering the period 1 January 2020 and 21 August 2020, following predefined eligibility criteria. Forty scientific papers met our inclusion criteria and were included in the review.

RESULTS

The mean of the serial interval ranged from 3.03 to 7.6 days, based on 38 estimates, and the median from 1.0 to 6.0 days (based on 15 estimates). Only three estimates were provided for the mean of the generation time. These ranged from 3.95 to 5.20 days. One estimate of 5.0 days was provided for the median of the generation time.

DISCUSSION

Estimates of the serial interval and the generation time are very dependent on the specific factors that apply at the time that the data are collected, including the level of social contact. Consequently, the estimates may not be entirely relevant to other environments. Therefore, local estimates should be obtained as soon as possible. Careful consideration should be given to the methodology that is used. Real-time estimations of the serial interval/generation time, allowing for variations over time, may provide more accurate estimates of reproduction numbers than using conventionally fixed serial interval/generation time distributions.