Ensemble Forecast of COVID-19 for Vulnerability Assessment and Policy Interventions

Abstract The COVID-19 pandemic necessitates forecasts to frame science-informed policies. An accurate forecast of the size and timing of future waves could help public health officials and governments to plan appropriate responses. An ensemble forecast by aggregating different scenarios and models makes the prediction robust and reliable. We present an ensemble forecast for Wave-3 of COVID-19 in the state of Karnataka, India, using the IISc Population Balance Model for infectious disease spread. The reported data of confirmed, recovered, and deceased cases in Karnataka from 1 July 2020 to 4 July 2021 are utilized to tune the model’s parameters. An ensemble forecast is done from 5 July 2021 to 30 June 2022. The ensemble is built with 972 members by varying seven critical parameters that quantify the uncertainty in the spread dynamics (antibody waning, viral mutation) and interventions (pharmaceutical, non-pharmaceutical). The probability of Wave-3, the peak date distribution, and the peak caseload distribution are estimated from the ensemble forecast. Analysis of the ensemble forecast results shows that compliance to COVID-appropriate behaviour, daily vaccination rate, and emergence time of immune-escape new variants are the most significant causal factors that determine the timing and severity of COVID-19 Wave-3. We observe that when compliance to COVID-appropriate behaviour is similar to a lockdown-like situation, the emergence of new immune-escape variants beyond September 2021 is unlikely to induce a new wave. No or partial compliance to COVID-appropriate behaviour makes a new wave inevitable. However, increasing the vaccination rate reduces the active caseload at Wave-3’s peak. If Wave-3 emerges, on average, the daily confirmed caseload of children (Age 0–17 years) could be up to seven times more than the corresponding caseload (4390) at Wave-2’s peak. Therefore, large-scale surveillance, including genome sequencing for early detection of new variants and non-pharmaceutical interventions to improve COVID-Appropriate behaviour, is vital to prevent Wave-3 of COVID-19. Doubling the vaccination rate as of 4 July 2021 to 560K doses per day will reduce the daily confirmed cases even if Wave-3 arises. Consequently, hospitalizations, ICU, and Oxygen requirements can be decreased. Since vaccination is yet to start in children, it is essential to ramp up the public health facilities, including pediatric ICUs to treat MIS-C, by 5-9 times to handle the worst-case situation. From a modeling perspective, capturing the nonlinear dynamics induced by the uncertainties in the causal factors is the key to a successful forecast. Therefore, an effort should be made to build an ensemble forecast that contains multiple models and, more importantly, models that account for causal factor uncertainties.

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Ensemble forecast of COVID-19 in Karnataka for vulnerability assessment and policy interventions

10.1101/2021.08.31.21262943 ◽

2021 ◽

Author(s):

Sashikumaar Ganesan ◽

Deepak Subramani ◽

Thivin Anandh ◽

Divij Ghose ◽

Giridhara R Babu

Keyword(s):

Immune Escape ◽

Ensemble Forecast ◽

Disease Spread ◽

Critical Parameters ◽

New Wave ◽

Public Health Response ◽

Causal Factors ◽

Appropriate Behavior ◽

Policy Interventions ◽

New Variant

We present an ensemble forecast for Wave-3 of COVID-19 in the state of Karnataka, India, using the IISc Population Balance Model for infectious disease spread. The reported data of confirmed, recovered, and deceased cases in Karnataka from 1 July 2020 to 4 July 2021 is utilized to tune the model's parameters, and an ensemble forecast is done from 5 July 2021 to 30 June 2022. The ensemble is built with 972 members by varying seven critical parameters that quantify the uncertainty in the spread dynamics (antibody waning, viral mutation) and interventions (pharmaceutical, non-pharmaceutical). The probability of Wave-3, the peak date distribution, and the peak caseload distribution are estimated from the ensemble forecast. Our analysis shows that the most significant causal factors are compliance to Covid-appropriate behavior, daily vaccination rate, and the immune escape new variant emergence-time. These causal factors determine when and how severe the Wave-3 of COVID-19 would be in Karnataka. We observe that when compliance to Covid-Appropriate Behavior is good (i.e., lockdown-like compliance), the emergence of new immune-escape variants beyond Sep '21 is unlikely to induce a new wave. A new wave is inevitable when compliance to Covid-Appropriate Behavior is only partial. Increasing the daily vaccination rates reduces the peak active caseload at Wave-3. Consequently, the hospitalization, ICU, and Oxygen requirements also decrease. Compared to Wave-2, the ensemble forecast indicates that the number of daily confirmed cases of children (0-17 years) at Wave-3's peak could be seven times more on average. Our results provide insights to plan science-informed policy interventions and public health response.

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Understanding Economic and Health Factors Impacting the Spread of COVID-19 Disease (Preprint)

10.2196/preprints.19386 ◽

2020 ◽

Author(s):

Aleksandr Farseev ◽

Yu-Yi Chu-Farseeva ◽

Yang Qi ◽

Daron Benjamin Loo

Keyword(s):

Public Health ◽

Quantitative Research ◽

Health Indicators ◽

Initial Study ◽

Disease Spread ◽

Factors Affecting ◽

Country Level ◽

Development Scenarios ◽

Health Factors ◽

Country Clusters

UNSTRUCTURED The rapid spread of the Coronavirus 2019 disease (COVID-19) had drastically impacted life all over the world. While some economies are actively recovering from this pestilence, others are experiencing fast and consistent disease spread, compelling governments to impose social distancing measures that have put a halt on routines, especially in densely-populated areas. Aiming at bringing more light on key economic and public health factors affecting the disease spread, this initial study utilizes a quantitative statistical analysis based on the most recent publicly-available COVID-19 datasets. The study had shown and explained multiple significant relationships between the COVID-19 data and other country-level statistics. We have also identified and statistically profiled four major country-level clusters with relation to different aspects of COVID-19 development and country-level economic and health indicators. Specifically, this study has identified potential COVID-19 under-reporting traits as well as various economic factors that impact COVID-19 Diagnosis, Reporting, and Treatment. Based on the country clusters, we have also described the four disease development scenarios, which are tightly knit to country-level economic and public health factors. Finally, we have highlighted the potential limitation of reporting and measuring COVID-19 and provided recommendations on further in-depth quantitative research.

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The case for tracking misinformation the way we track disease

Big Data & Society ◽

10.1177/20539517211013867 ◽

2021 ◽

Vol 8 (1) ◽

pp. 205395172110138

Author(s):

Erika Bonnevie ◽

Jennifer Sittig ◽

Joe Smyser

Keyword(s):

Public Health ◽

Real Time ◽

Supervised Machine Learning ◽

Disease Spread ◽

The Public ◽

Monitoring Tools ◽

Core Function ◽

Public Health Organizations ◽

Front Line Workers ◽

Media Data

While public health organizations can detect disease spread, few can monitor and respond to real-time misinformation. Misinformation risks the public’s health, the credibility of institutions, and the safety of experts and front-line workers. Big Data, and specifically publicly available media data, can play a significant role in understanding and responding to misinformation. The Public Good Projects uses supervised machine learning to aggregate and code millions of conversations relating to vaccines and the COVID-19 pandemic broadly, in real-time. Public health researchers supervise this process daily, and provide insights to practitioners across a range of disciplines. Through this work, we have gleaned three lessons to address misinformation. (1) Sources of vaccine misinformation are known; there is a need to operationalize learnings and engage the pro-vaccination majority in debunking vaccine-related misinformation. (2) Existing systems can identify and track threats against health experts and institutions, which have been subject to unprecedented harassment. This supports their safety and helps prevent the further erosion of trust in public institutions. (3) Responses to misinformation should draw from cross-sector crisis management best practices and address coordination gaps. Real-time monitoring and addressing misinformation should be a core function of public health, and public health should be a core use case for data scientists developing monitoring tools. The tools to accomplish these tasks are available; it remains up to us to prioritize them.

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An analysis of COVID-19 clusters in India

BMC Public Health ◽

10.1186/s12889-021-10491-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Pooja Sengupta ◽

Bhaswati Ganguli ◽

Sugata SenRoy ◽

Aditya Chatterjee

Keyword(s):

Public Health ◽

Cluster Analysis ◽

Population Density ◽

Case Studies ◽

Compartment Model ◽

Health Interventions ◽

Disease Spread ◽

Second Phase ◽

Public Health Interventions ◽

Homogeneous Groups

Abstract Background In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. Methods A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. Results The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. Conclusions The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

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Mandatory vaccination against COVID-19 would be supported by less than fifty per cent: A Twitter poll analysis on public health opinion. (Preprint)

10.2196/preprints.35754 ◽

2021 ◽

Author(s):

Valentin Ritschl ◽

Fabian Eibensteiner ◽

Erika Mosor ◽

Maisa Omara ◽

Lisa Sperl ◽

...

Keyword(s):

Public Health ◽

Social Media ◽

Vaccination Coverage ◽

Digital Health ◽

Developed Countries ◽

Vaccination Rate ◽

World Health ◽

Vaccination Coverage Rate ◽

Twitter Users ◽

Mandatory Vaccination

BACKGROUND On January 30, 2020, the World Health Organization (WHO) Emergency Committee declared the rapid worldwide spread of Coronavirus Disease 2019 (COVID-19) a global health emergency. By December 2020, the safety and efficacy of the first COVID-19 vaccines had been demonstrated. However, global vaccination coverage rates have remained below expectations. Mandatory vaccination is now being controversially discussed and has been enacted in some developed countries, while the vaccination rate is very low in many developing countries. We used the Twitter survey system as a viable method to quickly and comprehensively gather international public health insights on mandatory vaccination against COVID-19. OBJECTIVE The purpose of this study was to understand better the public's perception of mandatory COVID-19 vaccination in real-time utilizing Twitter polls. METHODS Two Twitter polls were developed to seek the public's opinion on the possibility of mandatory vaccination. The polls were pinned to the Digital Health and Patient Safety Platform's Twitter timeline for one week in mid-November 2021, three days after the official public announcement of mandatory COVID-19 vaccination in Austria. Twitter users were asked to participate and retweet the polls to reach the largest possible audience. RESULTS Our Twitter polls revealed two extremes on the topic of mandatory vaccination against COVID-19. Almost half of the respondents (49% [1,246/2,545]) favour mandatory vaccination, at least in certain areas. This attitude is in contrast to the 45.7% (1,162/2,545) who categorically reject mandatory vaccination. 26.3% (621/2,365) of participating Twitter users said they would never get vaccinated, which is reflected by the current vaccination coverage rate. Concatenating interpretation of these two polls needs to be done cautiously as participating populations might greatly differ. CONCLUSIONS Mandatory vaccination against COVID-19 (in at least certain areas) is favoured by less than 50%, whereas it is vehemently opposed by almost half of the surveyed Twitter users. Since (social) media strongly influences public perceptions and views through and social media discussions and surveys specifically susceptible to the "echo chamber effect", the results can be seen as a momentary snapshot. Therefore, the results of this study need to be complemented by long-term surveys to maintain their validity.

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Spatiotemporal patterns and progression of the Delta variant of COVID-19 and their health intervention linkages in Southeast Asia

10.1101/2021.12.20.21268140 ◽

2021 ◽

Author(s):

Wei Luo ◽

Zhaoyin Liu ◽

Yuxuan Zhou ◽

Yumin Zhao ◽

Yunyue Elita Li ◽

...

Keyword(s):

Public Health ◽

Southeast Asia ◽

Spatiotemporal Analysis ◽

Health Interventions ◽

Disease Spread ◽

Epidemic Dynamics ◽

Health Measures ◽

Global Pandemic ◽

Public Health Strategies ◽

Time Scan

The global pandemic of COVID-19 presented an unprecedented challenge to all countries in the world, among which Southeast Asia (SEA) countries managed to maintain and mitigate the first wave of COVID-19 in 2020. However, these countries were caught in the crisis after the Delta variant was introduced to SEA, though many countries had immediately implemented non-pharmaceutical intervention (NPI) measures along with vaccination in order to contain the disease spread. To investigate the potential linkages between epidemic dynamics and public health interventions, we adopted a prospective space-time scan method to conduct spatiotemporal analysis at the district level in the seven selected countries in SEA from June 2021 to October 2021. Results reveal the spatial and temporal propagation and progression of COVID-19 risks relative to public health measures implemented by different countries. Our research benefits continuous improvements of public health strategies in preventing and containing this pandemic.

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ANALYSIS OF IMMUNE ESCAPE VARIANTS FROM ANTIBODY-BASED THERAPEUTICS AGAINST COVID-19.

10.1101/2021.11.11.21266207 ◽

2021 ◽

Author(s):

Daniele Focosi ◽

Fabrizio Maggi ◽

Massimo Franchini ◽

Scott McConnell ◽

Arturo Casadevall

Keyword(s):

Public Health ◽

Monoclonal Antibodies ◽

Immune Evasion ◽

Immune Escape ◽

Selective Pressure ◽

Neutralizing Antibody ◽

Spike Protein ◽

Single Amino Acid ◽

Receptor Binding Domain ◽

Amino Acid Mutations

Accelerated SARS-CoV-2 evolution under selective pressure by massive deployment of neutralizing antibody-based therapeutics is a concern with potentially severe implications for public health. We review here reports of documented immune escape after treatment with monoclonal antibodies and COVID19 convalescent plasma (CCP). While the former is mainly associated with specific single amino acid mutations at residues within the receptor-binding domain (e.g., E484K/Q, Q493R, and S494P), the few cases of immune evasion after CCP were associated with recurrent deletions within the N-terminal domain of Spike protein (e.g, delHV69-70, delLGVY141-144 and delAL243-244). Continuous genomic monitoring of non-responders is needed to better understand immune escape frequencies and fitness of emerging variants.

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A window of opportunity for intensifying testing and tracing efforts to prevent new COVID-19 outbreaks due to more transmissible variants

Canada Communicable Disease Report ◽

10.14745/ccdr.v47i78a06 ◽

2021 ◽

Vol 47 (7/8) ◽

pp. 329-338

Author(s):

Jianhong Wu ◽

Francesca Scarabel ◽

Zachary McCarthy ◽

Yanyu Xiao ◽

Nicholas H Ogden

Keyword(s):

Public Health ◽

Reproduction Number ◽

Health Interventions ◽

Contact Tracing ◽

Window Of Opportunity ◽

New Wave ◽

Public Health Interventions ◽

Contact Rates ◽

Large Outbreak ◽

Restrictive Measures

Background: When public health interventions are being loosened after several days of decline in the number of coronavirus disease 2019 (COVID-19) cases, it is of critical importance to identify potential strategies to ease restrictions while mitigating a new wave of more transmissible variants of concern (VOCs). We estimated the necessary enhancements to public health interventions for a partial reopening of the economy while avoiding the worst consequences of a new outbreak, associated with more transmissible VOCs. Methods: We used a transmission dynamics model to quantify conditions that combined public health interventions must meet to reopen the economy without a large outbreak. These conditions are those that maintain the control reproduction number below unity, while accounting for an increase in transmissibility due to VOC. Results: We identified combinations of the proportion of individuals exposed to the virus who are traced and quarantined before becoming infectious, the proportion of symptomatic individuals confirmed and isolated, and individual daily contact rates needed to ensure the control reproduction number remains below unity. Conclusion: Our analysis indicates that the success of restrictive measures including lockdown and stay-at-home orders, as reflected by a reduction in number of cases, provides a narrow window of opportunity to intensify case detection and contact tracing efforts to prevent a new wave associated with circulation of more transmissible VOCs.

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Healthcare Students’ Perception of Social Distancing during the 2019 Coronavirus Pandemic: A Cross-Sectional Survey

International Journal of Medical Students ◽

10.5195/ijms.2021.820 ◽

2021 ◽

Author(s):

Devon L Barrett ◽

Katharine W Rainer ◽

Chao Zhang ◽

Travis W Blalock

Keyword(s):

Public Health ◽

Disease Spread ◽

Cross Sectional Survey ◽

Cross Sectional ◽

Social Distancing ◽

Healthcare Students ◽

Common Definition ◽

Grouping Students ◽

Control And Prevention ◽

Definition Of

Background: Since the implementation of social distancing practices during the global coronavirus disease 2019 (COVID-19) pandemic there have been a myriad of definitions for ‘social distancing.’ The objective of this study was to determine students’ awareness of the various definitions of social distancing, how strictly they adhered to social distancing guidelines, and how they perceived the importance of various social distancing practices. Methods: This cross-sectional survey was distributed via email to students at Emory-affiliated graduate schools, including the Medical, Nursing, and Public Health Schools. Results: Of the 2,453 recipients of the survey, 415 students responded (16.9% response rate). The majority of respondents were medical students (n=225, 55.6%). Of the respondents, 357 noted that they “frequently” or “always” abided by social distancing. The most common definition of social distancing with which respondents were familiar was the Centers for Disease Control and Prevention (CDC)’s (n=276 of 369 responses, 74.8%). There were significant differences across groups when grouping students by the definition of social distancing that they were aware of, the social distancing guideline they most closely followed, and their school of attendance regarding the importance of specific social distancing examples (p<0.05 for each). Conclusions: A survey of healthcare students identified differences in the importance of social distancing practices based on the definition of social distancing that they were aware of. The results of this study underscore the importance of having unified definitions of public health messaging, which ultimately may impact disease spread.

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Disinfection performance of a drinking water bottle system with a UVC LED cap against waterborne pathogens and heterotrophic contaminants

10.1101/2021.06.02.446792 ◽

2021 ◽

Author(s):

Richard M Mariita ◽

Sebastien A Blumenstein ◽

Christian M Beckert ◽

Thomas Gombas ◽

Rajul V Randive

Keyword(s):

Public Health ◽

16S Rrna ◽

16S Rrna Gene ◽

Heterotrophic Bacteria ◽

Health Concern ◽

Plate Count ◽

Rrna Gene ◽

Public Health Concern ◽

Worst Case ◽

The 16S Rrna Gene

Background: The purgaty One systems (cap+bottle) are portable stainless-steel water bottles with ultraviolet subtype C (UVC) disinfection capability. This study examines the bottle design, verifies disinfection performance against Escherichia coli, Pseudomonas aeruginosa, Vibrio cholerae and heterotrophic contaminants and addresses the public health relevance of heterotrophic bacteria. Methods: Bottles were inoculated with bacterial strains and disinfection efficacy examined using colony forming unit (CFU) assay. The heterotrophic plate count (HPC) method was used to determine the disinfection performance against environmental contaminants at day 0 and after 3 days of water stagnation. All UVC irradiation experiments were performed under stagnant conditions to confirm that the preset application cycle of 55 seconds offers the desired disinfection performance under worst-case condition. To determine the effectiveness of purgaty One systems (cap+bottle) in disinfection, inactivation efficacy or log reduction value (LRV) was determined using bacteria concentration between UVC ON condition and controls (UVC OFF). The study utilized the 16S rRNA gene for isolate characterization by identifying HPC bacteria to confirm if they belong to groups that are of public health concern. Results: Purgaty One systems fitted with Klaran UVC LEDs achieved 99.99% inactivation (LRV4) efficacy against E. coli and 99.9% inactivation (LRV3) against P. aeruginosa, V. cholerae and heterotrophic contaminants. Based on the 16S rRNA gene analyses, the study determined that the identified HPC isolates enriched by UVC irradiation are of rare public health concern. Conclusion: The bottles satisfactorily inactivated the target pathogenic bacteria and HPC contaminants even after 3 days of water stagnation.

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