Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks.

Staff at public health departments have few training materials to learn how to design and fine-tune systems to quickly detect acute, localized, community-acquired outbreaks of infectious diseases. Since 2014, the Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene has analyzed reportable communicable diseases daily using SaTScan. SaTScan is a free software that analyzes data using scan statistics, which can detect increasing disease activity without a priori specification of temporal period, geographic location, or size. The Bureau of Communicable Disease's systems have quickly detected outbreaks of salmonellosis, legionellosis, shigellosis, and COVID-19. This tutorial details system design considerations, including geographic and temporal data aggregation, study period length, inclusion criteria, whether to account for population size, network location file setup to account for natural boundaries, probability model (eg, space-time permutation), day-of-week effects, minimum and maximum spatial and temporal cluster sizes, secondary cluster reporting criteria, signaling criteria, and distinguishing new clusters versus ongoing clusters with additional events. We illustrate how to support health equity by minimizing analytic exclusions of patients with reportable diseases (eg, persons experiencing homelessness who are unsheltered) and accounting for purely spatial patterns, such as adjusting nonparametrically for areas with lower access to care and testing for reportable diseases. We describe how to fine-tune the system when the detected clusters are too large to be of interest or when signals of clusters are delayed, missed, too numerous, or false. We demonstrate low-code techniques for automating analyses and interpreting results through built-in features on the user interface (eg, patient line lists, temporal graphs, and dynamic maps), which became newly available with the July 2022 release of SaTScan version 10.1. This tutorial is the first comprehensive resource for health department staff to design and maintain a reportable communicable disease outbreak detection system using SaTScan to catalyze field investigations as well as develop intuition for interpreting results and fine-tuning the system. While our practical experience is limited to monitoring certain reportable diseases in a dense, urban area, we believe that most recommendations are generalizable to other jurisdictions in the United States and internationally. Additional analytic technical support for detecting outbreaks would benefit state, tribal, local, and territorial public health departments and the populations they serve.

Pfizer-BioNTech Coronavirus Disease 2019 Vaccine Effectiveness Against Severe Acute Respiratory Syndrome Coronavirus 2 Infection Among Long-term Care Facility Staff With and Without Prior Infection in New York City, January-June 2021.

BACKGROUND: Evidence is accumulating of coronavirus disease 2019 (COVID-19) vaccine effectiveness among persons with prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. METHODS: We evaluated the effect against incident SARS-CoV-2 infection of (1) prior infection without vaccination, (2) vaccination (2 doses of Pfizer-BioNTech COVID-19 vaccine) without prior infection, and (3) vaccination after prior infection, all compared with unvaccinated persons without prior infection. We included long-term care facility staff in New York City aged <65 years with weekly SARS-CoV-2 testing from 21 January to 5 June 2021. Test results were obtained from state-mandated laboratory reporting. Vaccination status was obtained from the Citywide Immunization Registry. Cox proportional hazards models adjusted for confounding with inverse probability of treatment weights. RESULTS: Compared with unvaccinated persons without prior infection, incident SARS-CoV-2 infection risk was lower in all groups: 54.6% (95% confidence interval, 38.0%-66.8%) lower among unvaccinated, previously infected persons; 80.0% (67.6%-87.7%) lower among fully vaccinated persons without prior infection; and 82.4% (70.8%-89.3%) lower among persons fully vaccinated after prior infection. CONCLUSIONS: Two doses of Pfizer-BioNTech COVID-19 vaccine reduced SARS-CoV-2 infection risk by ≥80% and, for those with prior infection, increased protection from prior infection alone. These findings support recommendations that all eligible persons, regardless of prior infection, be vaccinated against COVID-19.

Reduced Odds of Severe Acute Respiratory Syndrome Coronavirus 2 Reinfection After Vaccination Among New York City Adults, July 2021-November 2021.

BACKGROUND: Belief that vaccination is not needed for individuals with prior infection contributes to coronavirus disease 2019 (COVID-19) vaccine hesitancy. Among individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) before vaccines became available, we determined whether vaccinated individuals had reduced odds of reinfection. METHODS: We conducted a case-control study among adult New York City residents who tested positive for SARS-CoV-2 infection in 2020 and had not died or tested positive again >90 days after an initial positive test as of 1 July 2021. Case patients with reinfection during July 2021-November 2021 and controls with no reinfection were matched (1:3) on age, sex, timing of initial positive test in 2020, and neighborhood poverty level. Matched odds ratios (mORs) and 95% confidence intervals (CIs) were calculated using conditional logistic regression. RESULTS: Of 349 827 eligible adults, 2583 were reinfected during July 2021-November 2021. Of 2401 with complete matching criteria data, 1102 (45.9%) were known to be symptomatic for COVID-19-like illness, and 96 (4.0%) were hospitalized. Unvaccinated individuals, compared with individuals fully vaccinated within the prior 90 days, had elevated odds of reinfection (mOR, 3.21; 95% CI, 2.70 to 3.82), of symptomatic reinfection (mOR, 2.97; 95% CI, 2.31 to 3.83), and of reinfection with hospitalization (mOR, 2.09; 95% CI, .91 to 4.79). CONCLUSIONS: Vaccination reduced odds of reinfections when the Delta variant predominated. Further studies should assess risk of severe outcomes among reinfected persons as new variants emerge, infection- and vaccine-induced immunity wanes, and booster doses are administered.

Effects of Return-to-Office, Public Schools Reopening, and Vaccination Mandates on COVID-19 Cases Among Municipal Employee Residents of New York City.

OBJECTIVE: On September 13, 2021, teleworking ended for New York City municipal employees, and Department of Education employees returned to reopened schools. On October 29, COVID-19 vaccination was mandated. We assessed these mandates' short-term effects on disease transmission. METHODS: Using difference-in-difference analyses, we calculated COVID-19 incidence rate ratios (IRRs) among residents 18 to 64 years old by employment status before and after policy implementation. RESULTS: IRRs after (September 23-October 28) versus before (July 5-September 12) the return-to-office mandate were similar between office-based City employees and non-City employees. Among Department of Education employees, the IRR after schools reopened was elevated by 28.4% (95% confidence interval, 17.3%-40.3%). Among City employees, the IRR after (October 29-November 30) versus before (September 23-October 28) the vaccination mandate was lowered by 20.1% (95% confidence interval, 13.7%-26.0%). CONCLUSIONS: Workforce mandates influenced disease transmission, among other societal effects.

Comparative hospitalization risk for SARS-CoV-2 Omicron and Delta variant infections, by variant predominance periods and patient-level sequencing results, New York City, August 2021-January 2022.

BACKGROUND: Comparing disease severity between SARS-CoV-2 variants among populations with varied vaccination and infection histories can help characterize emerging variants and support healthcare system preparedness. METHODS: We compared COVID-19 hospitalization risk among New York City residents with positive laboratory-based SARS-CoV-2 tests when ≥98% of sequencing results were Delta (August-November 2021) or Omicron (BA.1 and sublineages, January 2022). A secondary analysis defined variant exposure using patient-level sequencing results during July 2021-January 2022, comprising 1-18% of weekly confirmed cases. RESULTS: Hospitalization risk was lower among patients testing positive when Omicron (16,025/488,053, 3.3%) than when Delta predominated (8268/158,799, 5.2%). In multivariable analysis adjusting for demographic characteristics and prior diagnosis and vaccination status, patients testing positive when Omicron predominated, compared with Delta, had 0.72 (95% CI: 0.63, 0.82) times the hospitalization risk. In a secondary analysis of patients with sequencing results, hospitalization risk was similar among patients infected with Omicron (2042/29,866, 6.8%), compared with Delta (1780/25,272, 7.0%), and higher among the subset who received two mRNA vaccine doses (adjusted relative risk 1.64; 95% CI: 1.44, 1.87). CONCLUSIONS: Hospitalization risk was lower among patients testing positive when Omicron predominated, compared with Delta. This finding persisted after adjusting for prior diagnosis and vaccination status, suggesting intrinsic virologic properties, not population-based immunity, explained the lower severity. Secondary analyses demonstrated collider bias from the sequencing sampling frame changing over time in ways associated with disease severity. Representative data collection is necessary to avoid bias when comparing disease severity between previously dominant and newly emerging variants.

Epidemiological characteristics of the B.1.526 SARS-CoV-2 variant.

To characterize the epidemiological properties of the B.1.526 SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) variant of interest, here we used nine epidemiological and population datasets and model-inference methods to reconstruct SARS-CoV-2 transmission dynamics in New York City, where B.1.526 emerged. We estimated that B.1.526 had a moderate increase (15 to 25%) in transmissibility, could escape immunity in 0 to 10% of previously infected individuals, and substantially increased the infection fatality risk (IFR) among adults 65 or older by >60% during November 2020 to April 2021, compared to estimates for preexisting variants. Overall, findings suggest that new variants like B.1.526 likely spread in the population weeks before detection and that partial immune escape (e.g., resistance to therapeutic antibodies) could offset prior medical advances and increase IFR. Early preparedness for and close monitoring of SARS-CoV-2 variants, their epidemiological characteristics, and disease severity are thus crucial to COVID-19 (coronavirus disease 2019) response.

Reduced COVID-19 hospitalizations among New York City residents following age-based SARS-CoV-2 vaccine eligibility: Evidence from a regression discontinuity design.

BACKGROUND: In clinical trials, several SARS-CoV-2 vaccines were shown to reduce risk of severe COVID-19 illness. Local, population-level, real-world evidence of vaccine effectiveness is accumulating. We assessed vaccine effectiveness for community-dwelling New York City (NYC) residents using a quasi-experimental, regression discontinuity design, leveraging a period (January 12-March 9, 2021) when ≥ 65-year-olds were vaccine-eligible but younger persons, excluding essential workers, were not. METHODS: We constructed segmented, negative binomial regression models of age-specific COVID-19 hospitalization rates among 45-84-year-old NYC residents during a post-vaccination program implementation period (February 21-April 17, 2021), with a discontinuity at age 65 years. The relationship between age and hospitalization rates in an unvaccinated population was incorporated using a pre-implementation period (December 20, 2020-February 13, 2021). We calculated the rate ratio (RR) and 95% confidence interval (CI) for the interaction between implementation period (pre or post) and age-based eligibility (45-64 or 65-84 years). Analyses were stratified by race/ethnicity and borough of residence. Similar analyses were conducted for COVID-19 deaths. RESULTS: Hospitalization rates among 65-84-year-olds decreased from pre- to post-implementation periods (RR 0.85, 95% CI: 0.74-0.97), controlling for trends among 45-64-year-olds. Accordingly, an estimated 721 (95% CI: 126-1,241) hospitalizations were averted. Residents just above the eligibility threshold (65-66-year-olds) had lower hospitalization rates than those below (63-64-year-olds). Racial/ethnic groups and boroughs with higher vaccine coverage generally experienced greater reductions in RR point estimates. Uncertainty was greater for the decrease in COVID-19 death rates (RR 0.85, 95% CI: 0.66-1.10). CONCLUSION: The vaccination program in NYC reduced COVID-19 hospitalizations among the initially age-eligible ≥ 65-year-old population by approximately 15% in the first eight weeks. The real-world evidence of vaccine effectiveness makes it more imperative to improve vaccine access and uptake to reduce inequities in COVID-19 outcomes.

Risk Factors for Death Among Hospitalized Patients Aged 21-64 Years Diagnosed with COVID-19-New York City, March 13-April 9, 2020.

BACKGROUND: COVID-19 mortality studies have primarily focused on persons aged ≥ 65 years; less is known about decedents aged <65 years. METHODS: We conducted a case-control study among NYC residents aged 21-64 years hospitalized with COVID-19 diagnosed March 13-April 9, 2020, to determine risk factors for death. Case-patients (n=343) were hospitalized decedents with COVID-19 and control-patients (n=686) were discharged from hospitalization with COVID-19 and matched 2:1 to case-patients on age and residential neighborhood. Conditional logistic regression models were adjusted for patient sex, insurance status, and marital status. Matched adjusted odds ratios (aORs) were calculated for selected underlying conditions, combinations of conditions, and race/ethnic group. RESULTS: Median age of both case-patients and control-patients was 56 years (range: 23-64 years). Having ≥ 1 selected underlying condition increased odds of death 4.45-fold (95% CI: 2.33-8.49). Patients with diabetes; morbid obesity; heart, kidney, or lung disease; cancer; neurologic/neurodevelopmental conditions; mental health conditions; or HIV had significantly increased odds of death. Compared with having neither condition, having both diabetes and obesity or diabetes and heart disease was associated with approximately threefold odds of death. Five select underlying conditions were more prevalent among non-Hispanic Black control-patients than among control-patients of other races/ethnicities. CONCLUSIONS AND RELEVANCE: Selected underlying conditions were risk factors for death, and most prevalent among racial/ethnic minorities. Social services; health care resources, including vaccination; and tailored public health messaging are important for COVID-19 prevention. Strengthening these strategies for racial/ethnic minority groups could minimize COVID-19 racial/ethnic disparities.

Persistent disparities in SARS-CoV-2 test percent positivity by neighborhood in New York City, March 1-July 25, 2020.

PURPOSE: To examine neighborhood-level disparities in SARS-CoV-2 molecular test percent positivity in New York City (NYC) by demographics and socioeconomic status over time to better understand COVID-19 inequities. METHODS: Across 177 neighborhoods, we calculated the Spearman correlation of neighborhood characteristics with SARS-CoV-2 molecular test percent positivity during March 1-July 25, 2020 by five periods defined by trend in case counts: increasing, declining, and three plateau periods to account for differential testing capacity and reopening status. RESULTS: Percent positivity was positively correlated with neighborhood racial and ethnic characteristics and socioeconomic status, including the proportion of the population who were Latino and Black non-Latino, uninsured, Medicaid enrollees, transportation workers, or had low educational attainment. Correlations were generally consistent over time despite increasing testing rates. Neighborhoods with high proportions of these correlates had median percent positivity values of 62.6%, 28.7%, 6.4%, 2.8%, and 2.2% in the five periods, respectively, compared with 40.6%, 11.7%, 1.7%, 0.9%, and 1.0% in neighborhoods with low proportions of these correlates. CONCLUSIONS: Disparities in SARS-CoV-2 molecular test percent positivity persisted in disadvantaged neighborhoods during multiple phases of the first few months of the COVID-19 epidemic in NYC. Mitigation of the COVID-19 burden is still urgently needed in disproportionately affected communities.

Detecting COVID-19 Clusters at High Spatiotemporal Resolution, New York City, New York, USA, June-July 2020.

A surveillance system that uses census tract resolution and the SaTScan prospective space-time scan statistic detected clusters of increasing severe acute respiratory syndrome coronavirus 2 test percent positivity in New York City, NY, USA. Clusters included one in which patients attended the same social gathering and another that led to targeted testing and outreach.

Population-Based Estimates of Coronavirus Disease 2019 (COVID-19)-like Illness, COVID-19 Illness, and Rates of Case Ascertainment, Hospitalizations, and Deaths-Noninstitutionalized New York City Residents, March-April 2020.

Using a population-based, representative telephone survey, ~930 000 New York City residents had COVID-19 illness beginning 20 March-30 April 2020, a period with limited testing. For every 1000 persons estimated with COVID-19 illness, 141.8 were tested and reported as cases, 36.8 were hospitalized, and 12.8 died, varying by demographic characteristics.

Nowcasting for Real-Time COVID-19 Tracking in New York City: An Evaluation Using Reportable Disease Data From Early in the Pandemic.

BACKGROUND: Nowcasting approaches enhance the utility of reportable disease data for trend monitoring by correcting for delays, but implementation details affect accuracy. OBJECTIVE: To support real-time COVID-19 situational awareness, the New York City Department of Health and Mental Hygiene used nowcasting to account for testing and reporting delays. We conducted an evaluation to determine which implementation details would yield the most accurate estimated case counts. METHODS: A time-correlated Bayesian approach called Nowcasting by Bayesian Smoothing (NobBS) was applied in real time to line lists of reportable disease surveillance data, accounting for the delay from diagnosis to reporting and the shape of the epidemic curve. We retrospectively evaluated nowcasting performance for confirmed case counts among residents diagnosed during the period from March to May 2020, a period when the median reporting delay was 2 days. RESULTS: Nowcasts with a 2-week moving window and a negative binomial distribution had lower mean absolute error, lower relative root mean square error, and higher 95% prediction interval coverage than nowcasts conducted with a 3-week moving window or with a Poisson distribution. Nowcasts conducted toward the end of the week outperformed nowcasts performed earlier in the week, given fewer patients diagnosed on weekends and lack of day-of-week adjustments. When estimating case counts for weekdays only, metrics were similar across days when the nowcasts were conducted, with Mondays having the lowest mean absolute error of 183 cases in the context of an average daily weekday case count of 2914. CONCLUSIONS: Nowcasting using NobBS can effectively support COVID-19 trend monitoring. Accounting for overdispersion, shortening the moving window, and suppressing diagnoses on weekends-when fewer patients submitted specimens for testing-improved the accuracy of estimated case counts. Nowcasting ensured that recent decreases in observed case counts were not overinterpreted as true declines and supported officials in anticipating the magnitude and timing of hospitalizations and deaths and allocating resources geographically.

Estimating the infection-fatality risk of SARS-CoV-2 in New York City during the spring 2020 pandemic wave: a model-based analysis.

BACKGROUND: As the COVID-19 pandemic continues to unfold, the infection-fatality risk (ie, risk of death among all infected individuals including those with asymptomatic and mild infections) is crucial for gauging the burden of death due to COVID-19 in the coming months or years. Here, we estimate the infection-fatality risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in New York City, NY, USA, the first epidemic centre in the USA, where the infection-fatality risk remains unclear. METHODS: In this model-based analysis, we developed a meta-population network model-inference system to estimate the underlying SARS-CoV-2 infection rate in New York City during the 2020 spring pandemic wave using available case, mortality, and mobility data. Based on these estimates, we further estimated the infection-fatality risk for all ages overall and for five age groups (<25, 25-44, 45-64, 65-74, and ≥75 years) separately, during the period March 1 to June 6, 2020 (ie, before the city began a phased reopening). FINDINGS: During the period March 1 to June 6, 2020, 205 639 people had a laboratory-confirmed infection with SARS-CoV-2 and 21 447 confirmed and probable COVID-19-related deaths occurred among residents of New York City. We estimated an overall infection-fatality risk of 1·39% (95% credible interval 1·04-1·77) in New York City. Our estimated infection-fatality risk for the two oldest age groups (65-74 and ≥75 years) was much higher than the younger age groups, with a cumulative estimated infection-fatality risk of 0·116% (0·0729-0·148) for those aged 25-44 years and 0·939% (0·729-1·19) for those aged 45-64 years versus 4·87% (3·37-6·89) for those aged 65-74 years and 14·2% (10·2-18·1) for those aged 75 years and older. In particular, weekly infection-fatality risk was estimated to be as high as 6·72% (5·52-8·01) for those aged 65-74 years and 19·1% (14·7-21·9) for those aged 75 years and older. INTERPRETATION: Our results are based on more complete ascertainment of COVID-19-related deaths in New York City than other places and thus probably reflect the true higher burden of death due to COVID-19 than that previously reported elsewhere. Given the high infection-fatality risk of SARS-CoV-2, governments must account for and closely monitor the infection rate and population health outcomes and enact prompt public health responses accordingly as the COVID-19 pandemic unfolds. FUNDING: National Institute of Allergy and Infectious Diseases, National Science Foundation Rapid Response Research Program, and New York City Department of Health and Mental Hygiene.

COVID-19 Outbreak - New York City, February 29-June 1, 2020.

New York City (NYC) was an epicenter of the coronavirus disease 2019 (COVID-19) outbreak in the United States during spring 2020 (1). During March-May 2020, approximately 203,000 laboratory-confirmed COVID-19 cases were reported to the NYC Department of Health and Mental Hygiene (DOHMH). To obtain more complete data, DOHMH used supplementary information sources and relied on direct data importation and matching of patient identifiers for data on hospitalization status, the occurrence of death, race/ethnicity, and presence of underlying medical conditions. The highest rates of cases, hospitalizations, and deaths were concentrated in communities of color, high-poverty areas, and among persons aged ≥75 years or with underlying conditions. The crude fatality rate was 9.2% overall and 32.1% among hospitalized patients. Using these data to prevent additional infections among NYC residents during subsequent waves of the pandemic, particularly among those at highest risk for hospitalization and death, is critical. Mitigating COVID-19 transmission among vulnerable groups at high risk for hospitalization and death is an urgent priority. Similar to NYC, other jurisdictions might find the use of supplementary information sources valuable in their efforts to prevent COVID-19 infections.

Evaluation of Nowcasting for Real-Time COVID-19 Tracking - New York City, March-May 2020.

To account for delays between specimen collection and report, the New York City Department of Health and Mental Hygiene used a time-correlated Bayesian nowcasting approach to support real-time COVID-19 situational awareness. We retrospectively evaluated nowcasting performance for case counts among residents diagnosed during March-May 2020, a period when the median reporting delay was 2 days. Nowcasts with a 2-week moving window and a negative binomial distribution had lower mean absolute error, lower relative root mean square error, and higher 95% prediction interval coverage than nowcasts conducted with a 3-week moving window or with a Poisson distribution. Nowcasts conducted toward the end of the week outperformed nowcasts performed earlier in the week, given fewer patients diagnosed on weekends and lack of day-of-week adjustments. When estimating case counts for weekdays only, metrics were similar across days the nowcasts were conducted, with Mondays having the lowest mean absolute error, of 183 cases in the context of an average daily weekday case count of 2,914. Nowcasting ensured that recent decreases in observed case counts were not overinterpreted as true declines and supported health department leadership in anticipating the magnitude and timing of hospitalizations and deaths and allocating resources geographically.

Detection and Genetic Characterization of Community-Based SARS-CoV-2 Infections - New York City, March 2020.

To limit introduction of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), the United States restricted travel from China on February 2, 2020, and from Europe on March 13. To determine whether local transmission of SARS-CoV-2 could be detected, the New York City (NYC) Department of Health and Mental Hygiene (DOHMH) conducted deidentified sentinel surveillance at six NYC hospital emergency departments (EDs) during March 1-20. On March 8, while testing availability for SARS-CoV-2 was still limited, DOHMH announced sustained community transmission of SARS-CoV-2 (1). At this time, twenty-six NYC residents had confirmed COVID-19, and ED visits for influenza-like illness* increased, despite decreased influenza virus circulation.† The following week, on March 15, when only seven of the 56 (13%) patients with known exposure histories had exposure outside of NYC, the level of community SARS-CoV-2 transmission status was elevated from sustained community transmission to widespread community transmission (2). Through sentinel surveillance during March 1-20, DOHMH collected 544 specimens from patients with influenza-like symptoms (ILS)§ who had negative test results for influenza and, in some instances, other respiratory pathogens.¶ All 544 specimens were tested for SARS-CoV-2 at CDC; 36 (6.6%) tested positive. Using genetic sequencing, CDC determined that the sequences of most SARS-CoV-2-positive specimens resembled those circulating in Europe, suggesting probable introductions of SARS-CoV-2 from Europe, from other U.S. locations, and local introductions from within New York. These findings demonstrate that partnering with health care facilities and developing the systems needed for rapid implementation of sentinel surveillance, coupled with capacity for genetic sequencing before an outbreak, can help inform timely containment and mitigation strategies.

Salmonellosis Outbreak Detected by Automated Spatiotemporal Analysis - New York City, May-June 2019.

In May 2019, the New York City Department of Health and Mental Hygiene (NYCDOHMH) detected an unusual cluster of five salmonellosis patients via automated spatiotemporal analysis of notifiable diseases using free SaTScan software (1). Within 1 day of cluster detection, graduate student interviewers determined that three of the patients had eaten prepared food from the same grocery store (establishment A) located inside the cluster area. NYCDOHMH initiated an investigation to identify additional cases, establish the cause, and provide control recommendations. Overall, 15 New York City (NYC) residents with laboratory-diagnosed salmonellosis who reported eating food from establishment A were identified. The most commonly consumed food item was chicken, reported by 10 patients. All 11 clinical isolates available were serotyped as Salmonella Blockley, sequenced, and analyzed by core genome multilocus sequence typing; isolates had a median difference of zero alleles. Environmental assessments revealed food not held at the proper temperature, food not cooled properly, and potential cross-contamination during chicken preparation. Elevated fecal coliform counts were found in two of four ready-to-eat food samples collected from establishment A, and Bacillus cereus was detected in three. The outbreak strain of Salmonella was isolated from one patient's leftover chicken. Establishing automated spatiotemporal cluster detection analyses for salmonellosis and other reportable diseases could aid in the detection of geographically focused, community-acquired outbreaks even before laboratory subtyping results become available.

Detecting Drop-offs in Electronic Laboratory Reporting for Communicable Diseases in New York City.

CONTEXT: The Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene receives an average of more than 1000 reports daily via electronic laboratory reporting. Rapid recognition of any laboratory reporting drop-off of test results for 1 or more diseases is necessary to avoid delays in case investigation and outbreak detection. PROGRAM: We modified our outbreak detection approach using the prospective space-time permutation scan statistic in SaTScan. Instead of searching for spatiotemporal clusters of high case counts, we reconceptualized "space" as "laboratory" and instead searched for clusters of recent low reporting, overall and for each of 52 diseases and 10 hepatitis test types, within individual laboratories. Each analysis controlled for purely temporal trends affecting all laboratories and accounted for multiple testing. IMPLEMENTATION: A SAS program automatically created input files, invoked SaTScan, and further processed SaTScan analysis results and output summaries to a secure folder. Analysts reviewed output weekly and reported concerning drop-offs to coordinators, who liaised with reporting laboratory staff to investigate and resolve issues. EVALUATION: During a 42-week evaluation period, October 2017 to July 2018, we detected 62 unique signals of reporting drop-offs. Of these, 39 (63%) were verified as true drop-offs, including failures to generate or transmit files and programming errors. For example, a hospital laboratory stopped reporting influenza after changing a multiplex panel result from "positive" to "detected." Six drop-offs were detected despite low numbers of expected reports missing (<10 per drop-off). DISCUSSION: Our novel application of SaTScan identified a manageable number of possible electronic laboratory reporting drop-offs for investigation. Ongoing maintenance requirements are minimal but include accounting for laboratory mergers and referrals. Automated analyses facilitated rapid identification and correction of electronic laboratory reporting errors, even with small numbers of expected reports missing, suggesting that our approach might be generalizable to smaller jurisdictions.

Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples.

BACKGROUND: Infectious disease forecasting aims to predict characteristics of both seasonal epidemics and future pandemics. Accurate and timely infectious disease forecasts could aid public health responses by informing key preparation and mitigation efforts. MAIN BODY: For forecasts to be fully integrated into public health decision-making, federal, state, and local officials must understand how forecasts were made, how to interpret forecasts, and how well the forecasts have performed in the past. Since the 2013-14 influenza season, the Influenza Division at the Centers for Disease Control and Prevention (CDC) has hosted collaborative challenges to forecast the timing, intensity, and short-term trajectory of influenza-like illness in the United States. Additional efforts to advance forecasting science have included influenza initiatives focused on state-level and hospitalization forecasts, as well as other infectious diseases. Using CDC influenza forecasting challenges as an example, this paper provides an overview of infectious disease forecasting; applications of forecasting to public health; and current work to develop best practices for forecast methodology, applications, and communication. CONCLUSIONS: These efforts, along with other infectious disease forecasting initiatives, can foster the continued advancement of forecasting science.