Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada.

BACKGROUND: Emerging Infectious Diseases (EID) are a significant threat to population health globally. We aimed to examine the relationship between internet search engine queries and social media data on COVID-19 and determine if they can predict COVID-19 cases in Canada. METHODS: We analyzed Google Trends (GT) and Twitter data from 1/1/2020 to 3/31/2020 in Canada and used various signal-processing techniques to remove noise from the data. Data on COVID-19 cases was obtained from the COVID-19 Canada Open Data Working Group. We conducted time-lagged cross-correlation analyses and developed the long short-term memory model for forecasting daily COVID-19 cases. RESULTS: Among symptom keywords, "cough," "runny nose," and "anosmia" were strong signals with high cross-correlation coefficients >0.8 ( rCough = 0.825, t - 9; rRunnyNose = 0.816, t - 11; rAnosmia = 0.812, t - 3 ), showing that searching for "cough," "runny nose," and "anosmia" on GT correlated with the incidence of COVID-19 and peaked 9, 11, and 3 days earlier than the incidence peak, respectively. For symptoms- and COVID-related Tweet counts, the cross-correlations of Tweet signals and daily cases were rTweetSymptoms = 0.868, t - 11 and tTweetCOVID = 0.840, t - 10, respectively. The LSTM forecasting model achieved the best performance (MSE = 124.78, R2 = 0.88, adjusted R2 = 0.87) using GT signals with cross-correlation coefficients >0.75. Combining GT and Tweet signals did not improve the model performance. CONCLUSION: Internet search engine queries and social media data can be used as early warning signals for creating a real-time surveillance system for COVID-19 forecasting, but challenges remain in modelling.

Hospitalization for child maltreatment and other types of injury during the COVID-19 pandemic.

BACKGROUND: The possibility that child maltreatment was misclassified as unintentional injury during the COVID-19 pandemic has not been evaluated. OBJECTIVE: We assessed if child maltreatment hospitalizations changed during the pandemic, and if the change was accompanied by an increase in unintentional injuries. PARTICIPANTS AND SETTING: This study included children aged 0-4 years who were admitted for maltreatment or unintentional injuries between April 2006 and March 2021 in hospitals of Quebec, Canada. METHODS: We used interrupted time series regression to estimate the effect of the pandemic on hospitalization rates for maltreatment, compared with unintentional transport accidents, falls, and mechanical force injuries. We assessed if the change in maltreatment hospitalization was accompanied by an increase in specific types of unintentional injury. RESULTS: Hospitalizations for child maltreatment decreased from 16.3 per 100,000 (95 % CI 9.1-23.4) the year before the pandemic to 13.2 per 100,000 (95 % CI 6.7-19.7) during the first lockdown. Hospitalizations for most types of unintentional injury also decreased, but injuries due to falls involving another person increased from 9.0 to 16.5 per 100,000. Hospitalization rates for maltreatment and unintentional injury remained low during the second lockdown, but mechanical force injuries involving another person increased from 3.8 to 8.1 per 100,000. CONCLUSIONS: Hospitalizations for child maltreatment may have been misclassified as unintentional injuries involving another person during the pandemic. Children admitted for these types of unintentional injuries may benefit from closer assessment to rule out maltreatment.

Surveillance Systems for Monitoring Vaccination Coverage with Vaccines Recommended for Pregnant Women, United States.

Pregnant women* and their infants are at increased risk for serious influenza, pertussis, and COVID-19-related complications, including preterm birth, low-birth weight, and maternal and fetal death. The advisory committee on immunization practices recommends pregnant women receive tetanus-toxoid, reduced diphtheria toxoid, and acellular pertussis (Tdap) vaccine during pregnancy, and influenza and COVID-19 vaccines before or during pregnancy. Vaccination coverage estimates and factors associated with maternal vaccination are measured by various surveillance systems. The objective of this report is to provide a detailed overview of the following surveillance systems that can be used to assess coverage of vaccines recommended for pregnant women: Internet panel survey, National Health Interview Survey, National Immunization Survey-Adult COVID Module, Behavioral Risk Factor Surveillance System, Pregnancy Risk Assessment Monitoring System, Vaccine Safety Datalink, and MarketScan. Influenza, Tdap, and COVID-19 vaccination coverage estimates vary by data source, and select estimates are presented. Each surveillance system differs in the population of pregnant women, time period, geographic area for which estimates can be obtained, how vaccination status is determined, and data collected regarding vaccine-related knowledge, attitudes, behaviors, and barriers. Thus, multiple systems are useful for a more complete understanding of maternal vaccination. Ongoing surveillance from the various systems to obtain vaccination coverage and information regarding disparities and barriers related to vaccination are needed to guide program and policy improvements.

Quantifying excess mortality during the COVID-19 pandemic in 2020 in The Gambia: a time-series analysis of three health and demographic surveillance systems.

OBJECTIVES: Estimates for COVID-19-related excess mortality for African populations using local data are needed to design and implement effective control policies. METHODS: We applied time-series analysis using data from three health and demographic surveillance systems in The Gambia (Basse, Farafenni, and Keneba) to examine pandemic-related excess mortality during 2020, when the first SARS-CoV-2 wave was observed, compared to the pre-pandemic period (2016-2019). RESULTS: Across the three sites, average mortality during the pre-pandemic period and the total deaths during 2020 were 1512 and 1634, respectively (Basse: 1099 vs 1179, Farafenni: 316 vs 351, Keneba: 98 vs 104). The overall annual crude mortality rates per 100,000 (95% CI) were 589 (559, 619) and 599 (571, 629) for the pre-pandemic and 2020 periods, respectively. The adjusted excess mortality rate was 8.8 (-34.3, 67.6) per 100,000 person-month with the adjusted rate ratio (aRR) = 1.01 (0.94,1.11). The age-stratified analysis showed excess mortality in Basse for infants (aRR = 1.22 [1.04, 1.46]) and in Farafenni for the 65+ years age group (aRR = 1.19 [1, 1.44]). CONCLUSION: We did not find significant excess overall mortality in 2020 in The Gambia. However, some age groups may have been at risk of excess death. Public health response in countries with weak health systems needs to consider vulnerable age groups and the potential for collateral damage.

Monitoring Indonesian online news for COVID-19 event detection using deep learning

Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 monitoring because it reports almost every infectious disease incident globally. However, the news does not report only about COVID-19 events, but also various information related to COVID-19 topics such as the economic impact, health tips, and others. We developed a framework for online news monitoring and applied sentence classification for news titles using deep learning to distinguish between COVID-19 events and non-event news. The classification results showed that the fine-tuned bidirectional encoder representations from transformers (BERT) trained with Bahasa Indonesia achieved the highest performance (accuracy: 95.16%, precision: 94.71%, recall: 94.32%, F1-score: 94.51%). Interestingly, our framework was able to identify news that reports the new COVID strain from the United Kingdom (UK) as an event news, 13 days before the Indonesian officials closed the border for foreigners.

Extending and Strengthening Routine DHIS2 Surveillance Systems for COVID-19 Responses in Sierra Leone, Sri Lanka, and Uganda.

The COVID-19 pandemic challenged countries to protect their populations from this emerging disease. One aspect of that challenge was to rapidly modify national surveillance systems or create new systems that would effectively detect new cases of COVID-19. Fifty-five countries leveraged past investments in District Health Information Software version 2 (DHIS2) to quickly adapt their national public health surveillance systems for COVID-19 case reporting and response activities. We provide background on DHIS2 and describe case studies from Sierra Leone, Sri Lanka, and Uganda to illustrate how the DHIS2 platform, its community of practice, long-term capacity building, and local autonomy enabled countries to establish an effective COVID-19 response. With these case studies, we provide valuable insights and recommendations for strategies that can be used for national electronic disease surveillance platforms to detect new and emerging pathogens and respond to public health emergencies.