Adapting COVID-19 Contact Tracing Protocols to Accommodate Resource Constraints, Philadelphia, Pennsylvania, USA, 2021.

Because of constrained personnel time, the Philadelphia Department of Public Health (Philadelphia, PA, USA) adjusted its COVID-19 contact tracing protocol in summer 2021 by prioritizing recent cases and limiting staff time per case. This action reduced required staff hours to prevent each case from 21-30 to 8-11 hours, while maintaining program effectiveness.

Building a Simple Model to Assess the Impact of Case Investigation and Contact Tracing for Sexually Transmitted Diseases: Lessons From COVID-19.

Introduction: During the COVID-19 pandemic, the U.S. Centers for Disease Control and Prevention developed a simple spreadsheet-based tool to help state and local public health officials assess the performance and impact of COVID-19 case investigation and contact tracing in their jurisdiction. The applicability and feasibility of building such a tool for sexually transmitted diseases were assessed. Methods: The key epidemiologic differences between sexually transmitted diseases and respiratory diseases (e.g., mixing patterns, incubation period, duration of infection, and the availability of treatment) were identified, and their implications for modeling case investigation and contact tracing impact with a simple spreadsheet tool were remarked on. Existing features of the COVID-19 tool that are applicable for evaluating the impact of case investigation and contact tracing for sexually transmitted diseases were also identified. Results: Our findings offer recommendations for the future development of a spreadsheet-based modeling tool for evaluating the impact of sexually transmitted disease case investigation and contact tracing efforts. Generally, we advocate for simplifying sexually transmitted disease-specific complexities and performing sensitivity analyses to assess uncertainty. The authors also acknowledge that more complex modeling approaches might be required but note that it is possible that a sexually transmitted disease case investigation and contact tracing tool could incorporate features from more complex models while maintaining a user-friendly interface. Conclusions: A sexually transmitted disease case investigation and contact tracing tool could benefit from the incorporation of key features of the COVID-19 model, namely its user-friendly interface. The inherent differences between sexually transmitted diseases and respiratory viruses should not be seen as a limitation to the development of such tool.

Estimated Cases Averted by COVID-19 Digital Exposure Notification, Pennsylvania, USA, November 8, 2020-January 2, 2021.

We combined field-based data with mathematical modeling to estimate the effectiveness of smartphone-enabled COVID-19 exposure notification in Pennsylvania, USA. We estimated that digital notifications potentially averted 7-69 cases/1,000 notifications during November 8, 2020-January 2, 2021. Greater use and increased compliance could increase the effectiveness of digital notifications.

Estimated COVID-19 Cases and Hospitalizations Averted by Case Investigation and Contact Tracing in the US.

Importance: Evidence of the impact of COVID-19 case investigation and contact tracing (CICT) programs is lacking, but policy makers need this evidence to assess the value of such programs. Objective: To estimate COVID-19 cases and hospitalizations averted nationwide by US states' CICT programs. Design, Setting, and Participants: This decision analytical model study used combined data from US CICT programs (eg, proportion of cases interviewed, contacts notified or monitored, and days to case and contact notification) with incidence data to model outcomes of CICT over a 60-day period (November 25, 2020, to January 23, 2021). The study estimated a range of outcomes by varying assumed compliance with isolation and quarantine recommendations. Fifty-nine state and territorial health departments that received federal funding supporting COVID-19 pandemic response activities were eligible for inclusion. Data analysis was performed from July to September 2021. Exposure: Public health case investigation and contact tracing. Main Outcomes and Measures: The primary outcomes were numbers of cases and hospitalizations averted and the percentage of cases averted among cases not prevented by vaccination and other nonpharmaceutical interventions. Results: In total, 22 states and 1 territory reported all measures necessary for the analysis. These 23 jurisdictions covered 42.5% of the US population (approximately 140 million persons), spanned all 4 US Census regions, and reported data that reflected all 59 federally funded CICT programs. This study estimated that 1.11 million cases and 27 231 hospitalizations were averted by CICT programs under a scenario where 80% of interviewed cases and monitored contacts and 30% of notified contacts fully complied with isolation and quarantine guidance, eliminating their contributions to future transmission. As many as 1.36 million cases and 33 527 hospitalizations could have been prevented if all interviewed cases and monitored contacts had entered into and fully complied with isolation and quarantine guidelines upon being interviewed or notified. Across both scenarios and all jurisdictions, CICT averted an estimated median of 21.2% (range, 1.3%-65.8%) of the cases not prevented by vaccination and other nonpharmaceutical interventions. Conclusions and Relevance: These findings suggest that CICT programs likely had a substantial role in curtailing the pandemic in most jurisdictions during the 2020 to 2021 winter peak. Differences in outcomes across jurisdictions indicate an opportunity to further improve CICT effectiveness. These estimates demonstrate the potential benefits from sustaining and improving these programs.

Estimates of Cases and Hospitalizations Averted by COVID-19 Case Investigation and Contact Tracing in 14 Health Jurisdictions in the United States.

CONTEXT: The implementation of case investigation and contact tracing (CICT) for controlling COVID-19 (caused by SARS-CoV-2 virus) has proven challenging due to varying levels of public acceptance and initially constrained resources, especially enough trained staff. Evaluating the impacts of CICT will aid efforts to improve such programs. OBJECTIVES: Estimate the number of COVID-19 cases and hospitalizations averted by CICT and identify CICT processes that could improve overall effectiveness. DESIGN: We used data on the proportion of cases interviewed, contacts notified or monitored, and days from testing to case and contact notification from 14 jurisdictions to model the impact of CICT on cumulative case counts and hospitalizations over a 60-day period. Using the Centers for Disease Control and Prevention's COVIDTracer Advanced tool, we estimated a range of impacts by assuming either contacts would quarantine only if monitored or would do so upon notification of potential exposure. We also varied the observed program metrics to assess their relative influence. RESULTS: Performance by jurisdictions varied widely. Jurisdictions isolated between 12% and 86% of cases (including contacts that became cases) within 6 to 10 days after infection. We estimated that CICT-related reductions in transmission ranged from 0.4% to 32%. For every 100 remaining cases after other nonpharmaceutical interventions were implemented, CICT averted between 4 and 97 additional cases. Reducing time to case isolation by 1 day increased averted case estimates by up to 15 percentage points. Increasing the proportion of cases interviewed or contacts notified by 20 percentage points each resulted in at most 3 or 6 percentage point improvements in averted cases. CONCLUSIONS: We estimated that CICT reduced the number of COVID-19 cases and hospitalizations among all jurisdictions studied. Reducing time to isolation produced the greatest improvements in impact of CICT.

Disparities in Incidence of COVID-19 Among Underrepresented Racial/Ethnic Groups in Counties Identified as Hotspots During June 5-18, 2020 - 22 States, February-June 2020.

During January 1, 2020-August 10, 2020, an estimated 5 million cases of coronavirus disease 2019 (COVID-19) were reported in the United States.* Published state and national data indicate that persons of color might be more likely to become infected with SARS-CoV-2, the virus that causes COVID-19, experience more severe COVID-19-associated illness, including that requiring hospitalization, and have higher risk for death from COVID-19 (1-5). CDC examined county-level disparities in COVID-19 cases among underrepresented racial/ethnic groups in counties identified as hotspots, which are defined using algorithmic thresholds related to the number of new cases and the changes in incidence.† Disparities were defined as difference of ≥5% between the proportion of cases and the proportion of the population or a ratio ≥1.5 for the proportion of cases to the proportion of the population for underrepresented racial/ethnic groups in each county. During June 5-18, 205 counties in 33 states were identified as hotspots; among these counties, race was reported for ≥50% of cumulative cases in 79 (38.5%) counties in 22 states; 96.2% of these counties had disparities in COVID-19 cases in one or more underrepresented racial/ethnic groups. Hispanic/Latino (Hispanic) persons were the largest group by population size (3.5 million persons) living in hotspot counties where a disproportionate number of cases among that group was identified, followed by black/African American (black) persons (2 million), American Indian/Alaska Native (AI/AN) persons (61,000), Asian persons (36,000), and Native Hawaiian/other Pacific Islander (NHPI) persons (31,000). Examining county-level data disaggregated by race/ethnicity can help identify health disparities in COVID-19 cases and inform strategies for preventing and slowing SARS-CoV-2 transmission. More complete race/ethnicity data are needed to fully inform public health decision-making. Addressing the pandemic's disproportionate incidence of COVID-19 in communities of color can reduce the community-wide impact of COVID-19 and improve health outcomes.

Trends in Number and Distribution of COVID-19 Hotspot Counties - United States, March 8-July 15, 2020.

The geographic areas in the United States most affected by the coronavirus disease 2019 (COVID-19) pandemic have changed over time. On May 7, 2020, CDC, with other federal agencies, began identifying counties with increasing COVID-19 incidence (hotspots) to better understand transmission dynamics and offer targeted support to health departments in affected communities. Data for January 22-July 15, 2020, were analyzed retrospectively (January 22-May 6) and prospectively (May 7-July 15) to detect hotspot counties. No counties met hotspot criteria during January 22-March 7, 2020. During March 8-July 15, 2020, 818 counties met hotspot criteria for ≥1 day; these counties included 80% of the U.S. population. The daily number of counties meeting hotspot criteria peaked in early April, decreased and stabilized during mid-April-early June, then increased again during late June-early July. The percentage of counties in the South and West Census regions* meeting hotspot criteria increased from 10% and 13%, respectively, during March-April to 28% and 22%, respectively, during June-July. Identification of community transmission as a contributing factor increased over time, whereas identification of outbreaks in long-term care facilities, food processing facilities, correctional facilities, or other workplaces as contributing factors decreased. Identification of hotspot counties and understanding how they change over time can help prioritize and target implementation of U.S. public health response activities.

A dynamic modeling tool for estimating healthcare demand from the COVID19 epidemic and evaluating population-wide interventions.

OBJECTIVES: Public health officials need tools to assist in anticipating the healthcare resources required to confront the SARS-COV-2 pandemic. We constructed a modeling tool to aid active public health officials to estimate healthcare demand from the pandemic in their jurisdictions and to evaluate the potential impact of population-wide social-distancing interventions. METHODS: The tool uses an SEIR compartmental model to project the pandemic's local spread. Users input case counts, healthcare resources, and select intervention strategies to evaluate. Outputs include the number of infections and deaths with and without intervention, and the demand for hospital and critical care beds and ventilators relative to existing capacity. We illustrate the tool using data from three regions of Chile. RESULTS: Our scenarios indicate a surge in COVID-19 patients could overwhelm Chilean hospitals by June, peaking in July or August at six to 50 times the current supply of beds and ventilators. A lockdown strategy or combination of case isolation, home quarantine, social distancing of individuals >70 years, and telework interventions may keep treatment demand below capacity. CONCLUSIONS: Aggressive interventions can avert substantial morbidity and mortality from COVID-19. Our tool permits rapid evaluation of locally-applicable policy scenarios and updating of results as new data become available.

Estimating the impact of multiple immunization products on medically-attended respiratory syncytial virus (RSV) infections in infants.

BACKGROUND: Palivizumab, a monoclonal antibody and the only licensed immunization product for preventing respiratory syncytial virus (RSV) infection, is recommended for children with certain high-risk conditions. Other antibody products and maternal vaccines targeting young infants are in clinical development. Few studies have compared products closest to potential licensure and have primarily focused on the effects on hospitalizations only. Estimates of the impact of these products on medically-attended (MA) infections in a variety of healthcare settings are needed to assist with developing RSV immunization recommendations. METHODS: We developed a tool for practicing public health officials to estimate the impact of immunization strategies on RSV-associated MA lower respiratory tract infections (LRTIs) in various healthcare settings among infants <12 months. Users input RSV burden and seasonality and examine the influence of altering product efficacy and uptake assumptions. We used the tool to evaluate candidate products' impacts among a US birth cohort. RESULTS: We estimated without immunization, 407,360 (range: 339,650-475,980) LRTIs are attended annually in outpatient clinics, 147,240 (126,070-168,510) in emergency departments (EDs), and 33,180 (24,760-42,900) in hospitals. A passive antibody candidate targeting all infants prevented the most LRTIs: 196,470 (48% of visits without immunization) outpatient clinic visits (range: 163,810-229,650), 75,250 (51%) EDs visits (64,430-86,090), and 18,140 (55%) hospitalizations (13,770-23,160). A strategy combining maternal vaccine candidate and palivizumab prevented 58,210 (14% of visits without immunization) LRTIs in outpatient clinics (range: 48,520-67,970), 19,580 (13%) in EDs (16,760-22,400), and 8,190 (25%) hospitalizations (6,390-10,150). CONCLUSIONS: Results underscore the potential for anticipated products to reduce serious RSV illness. Our tool (provided to readers) can be used by different jurisdictions and accept updated data. Results can aid economic evaluations and public health decision-making regarding RSV immunization products.

Modeling Tool for Decision Support during Early Days of an Anthrax Event.

Health officials lack field-implementable tools for forecasting the effects that a large-scale release of Bacillus anthracis spores would have on public health and hospitals. We created a modeling tool (combining inhalational anthrax caseload projections based on initial case reports, effects of variable postexposure prophylaxis campaigns, and healthcare facility surge capacity requirements) to project hospitalizations and casualties from a newly detected inhalation anthrax event, and we examined the consequences of intervention choices. With only 3 days of case counts, the model can predict final attack sizes for simulated Sverdlovsk-like events (1979 USSR) with sufficient accuracy for decision making and confirms the value of early postexposure prophylaxis initiation. According to a baseline scenario, hospital treatment volume peaks 15 days after exposure, deaths peak earlier (day 5), and recovery peaks later (day 23). This tool gives public health, hospital, and emergency planners scenario-specific information for developing quantitative response plans for this threat.

Modeling in Real Time During the Ebola Response.

To aid decision-making during CDC's response to the 2014-2016 Ebola virus disease (Ebola) epidemic in West Africa, CDC activated a Modeling Task Force to generate estimates on various topics related to the response in West Africa and the risk for importation of cases into the United States. Analysis of eight Ebola response modeling projects conducted during August 2014-July 2015 provided insight into the types of questions addressed by modeling, the impact of the estimates generated, and the difficulties encountered during the modeling. This time frame was selected to cover the three phases of the West African epidemic curve. Questions posed to the Modeling Task Force changed as the epidemic progressed. Initially, the task force was asked to estimate the number of cases that might occur if no interventions were implemented compared with cases that might occur if interventions were implemented; however, at the peak of the epidemic, the focus shifted to estimating resource needs for Ebola treatment units. Then, as the epidemic decelerated, requests for modeling changed to generating estimates of the potential number of sexually transmitted Ebola cases. Modeling to provide information for decision-making during the CDC Ebola response involved limited data, a short turnaround time, and difficulty communicating the modeling process, including assumptions and interpretation of results. Despite these challenges, modeling yielded estimates and projections that public health officials used to make key decisions regarding response strategy and resources required. The impact of modeling during the Ebola response demonstrates the usefulness of modeling in future responses, particularly in the early stages and when data are scarce. Future modeling can be enhanced by planning ahead for data needs and data sharing, and by open communication among modelers, scientists, and others to ensure that modeling and its limitations are more clearly understood. The activities summarized in this report would not have been possible without collaboration with many U.S. and international partners (http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/partners.html).

Potential demand for respirators and surgical masks during a hypothetical influenza pandemic in the United States.

BACKGROUND: To inform planning for an influenza pandemic, we estimated US demand for N95 filtering facepiece respirators (respirators) by healthcare and emergency services personnel and need for surgical masks by pandemic patients seeking care. METHODS: We used a spreadsheet-based model to estimate demand for 3 scenarios of respirator use: base case (usage approximately follows epidemic curve), intermediate demand (usage rises to epidemic peak and then remains constant), and maximum demand (all healthcare workers use respirators from pandemic onset). We assumed that in the base case scenario, up to 16 respirators would be required per day per intensive care unit patient and 8 per day per general ward patient. Outpatient healthcare workers and emergency services personnel would require 4 respirators per day. Patients would require 1.2 surgical masks per day. RESULTS AND CONCLUSIONS: Assuming that 20% to 30% of the population would become ill, 1.7 to 3.5 billion respirators would be needed in the base case scenario, 2.6 to 4.3 billion in the intermediate demand scenario, and up to 7.3 billion in the maximum demand scenario (for all scenarios, between 0.1 and 0.4 billion surgical masks would be required for patients). For pandemics with a lower attack rate and fewer cases (eg, 2009-like pandemic), the number of respirators needed would be higher because the pandemic would have longer duration. Providing these numbers of respirators and surgical masks represents a logistic challenge for US public health agencies. Public health officials must urgently consider alternative use strategies for respirators and surgical masks during a pandemic that may vary from current practices.

Regional spread of Ebola virus, West Africa, 2014.

To explain the spread of the 2014 Ebola epidemic in West Africa, and thus help with response planning, we analyzed publicly available data. We found that the risk for infection in an area can be predicted by case counts, population data, and distances between affected and nonaffected areas.