COVID-19 Surveillance After Expiration of the Public Health Emergency Declaration - United States, May 11, 2023.

On January 31, 2020, the U.S. Department of Health and Human Services (HHS) declared, under Section 319 of the Public Health Service Act, a U.S. public health emergency because of the emergence of a novel virus, SARS-CoV-2.* After 13 renewals, the public health emergency will expire on May 11, 2023. Authorizations to collect certain public health data will expire on that date as well. Monitoring the impact of COVID-19 and the effectiveness of prevention and control strategies remains a public health priority, and a number of surveillance indicators have been identified to facilitate ongoing monitoring. After expiration of the public health emergency, COVID-19-associated hospital admission levels will be the primary indicator of COVID-19 trends to help guide community and personal decisions related to risk and prevention behaviors; the percentage of COVID-19-associated deaths among all reported deaths, based on provisional death certificate data, will be the primary indicator used to monitor COVID-19 mortality. Emergency department (ED) visits with a COVID-19 diagnosis and the percentage of positive SARS-CoV-2 test results, derived from an established sentinel network, will help detect early changes in trends. National genomic surveillance will continue to be used to estimate SARS-CoV-2 variant proportions; wastewater surveillance and traveler-based genomic surveillance will also continue to be used to monitor SARS-CoV-2 variants. Disease severity and hospitalization-related outcomes are monitored via sentinel surveillance and large health care databases. Monitoring of COVID-19 vaccination coverage, vaccine effectiveness (VE), and vaccine safety will also continue. Integrated strategies for surveillance of COVID-19 and other respiratory viruses can further guide prevention efforts. COVID-19-associated hospitalizations and deaths are largely preventable through receipt of updated vaccines and timely administration of therapeutics (1-4).

Mapathons versus automated feature extraction: a comparative analysis for strengthening immunization microplanning.

BACKGROUND: Social instability and logistical factors like the displacement of vulnerable populations, the difficulty of accessing these populations, and the lack of geographic information for hard-to-reach areas continue to serve as barriers to global essential immunizations (EI). Microplanning, a population-based, healthcare intervention planning method has begun to leverage geographic information system (GIS) technology and geospatial methods to improve the remote identification and mapping of vulnerable populations to ensure inclusion in outreach and immunization services, when feasible. We compare two methods of accomplishing a remote inventory of building locations to assess their accuracy and similarity to currently employed microplan line-lists in the study area. METHODS: The outputs of a crowd-sourced digitization effort, or mapathon, were compared to those of a machine-learning algorithm for digitization, referred to as automatic feature extraction (AFE). The following accuracy assessments were employed to determine the performance of each feature generation method: (1) an agreement analysis of the two methods assessed the occurrence of matches across the two outputs, where agreements were labeled as "befriended" and disagreements as "lonely"; (2) true and false positive percentages of each method were calculated in comparison to satellite imagery; (3) counts of features generated from both the mapathon and AFE were statistically compared to the number of features listed in the microplan line-list for the study area; and (4) population estimates for both feature generation method were determined for every structure identified assuming a total of three households per compound, with each household averaging two adults and 5 children. RESULTS: The mapathon and AFE outputs detected 92,713 and 53,150 features, respectively. A higher proportion (30%) of AFE features were befriended compared with befriended mapathon points (28%). The AFE had a higher true positive rate (90.5%) of identifying structures than the mapathon (84.5%). The difference in the average number of features identified per area between the microplan and mapathon points was larger (t = 3.56) than the microplan and AFE (t = - 2.09) (alpha = 0.05). CONCLUSIONS: Our findings indicate AFE outputs had higher agreement (i.e., befriended), slightly higher likelihood of correctly identifying a structure, and were more similar to the local microplan line-lists than the mapathon outputs. These findings suggest AFE may be more accurate for identifying structures in high-resolution satellite imagery than mapathons. However, they both had their advantages and the ideal method would utilize both methods in tandem.

A Survey to Assess Serological Prevalence of Poliovirus Antibodies in Areas With High-Risk for Vaccine-Derived Poliovirus Transmission in Chad.

BACKGROUND: World Health Organization African region is wild poliovirus-free; however, outbreaks of vaccine-derived poliovirus type 2 (VDPV2) continue to expand across the continent including in Chad. We conducted a serological survey of polio antibodies in polio high-risk areas of Chad to assess population immunity against poliovirus and estimate the risk of future outbreaks. METHODS: This was a community-based, cross-sectional survey carried out in September 2019. Children between 12 and 59 months were randomly selected using GIS enumeration of structures. Informed consent, demographic and anthropometric data, vaccination history, and blood spots were collected. Seropositivity against all 3 poliovirus serotypes was assessed using a microneutralization assay at Centers for Disease Control and Prevention, Atlanta, GA, USA. RESULTS: Analyzable data were obtained from 236 out of 285 (82.8%) enrolled children. Seroprevalence of polio antibodies for serotypes 1, 2, and 3 was 214/236 (90.7%); 145/236 (61.4%); and 196/236 (86.2%), respectively. For serotype 2, the seroprevalence significantly increased with age (P = .004); chronic malnutrition was a significant risk factor for being type 2-seronegative. INTERPRETATION: Poliovirus type 2 seroprevalence in young children was considered insufficient to protect against the spread of paralytic diseases caused by VDPV2. Indeed, VDPV2 outbreaks were reported from Chad in 2019 and 2020. High-quality immunization response to these outbreaks is needed to prevent further spread.

Community-based survey to assess seroprevalence of poliovirus antibodies in far-north Cameroon in 2020.

Background: This study assessed seroprevalence of poliovirus antibodies in children from selected poliovirus high-risk areas of the Far North region of Cameroon which serves to monitor polio immunization program. Methods: This was a community-based cross-sectional seroprevalence survey involving collection of dried blood specimens (DBS) among children aged 12-59 months (n = 401). Multi-stage cluster sampling using GIS was applied to select the study sample. Collected DBS were analysed with microneutralization assays for poliovirus neutralizing antibody levels. Results: The overall seroprevalence of types 1, 2 and 3 neutralizing antibodies were 86.8 % (95 % confidence interval [CI]: 83.1-89.8), 74.6 % (95 % CI: 70.1-78.6) and 79.3 % (95 % CI: 75.1-83.0), respectively. Median titers (log2 scale) for type 1, 2 and 3 were 7.17 (6.5-7.5), 5.17 (4.83-5.5), and 6.17 (5.5-6.5), respectively. There was an increasing trend in median titers and seroprevalence with age, statistically significant between the youngest and oldest age groups (p < 0.001). Conclusion: Though there were several opportunities for vaccination through supplementary immunization activities (SIA) and routine immunization (RI), seroprevalence levels were low for all three serotypes, particularly for type 2. This highlights the need to strengthen RI and SIA quality coverage. Low population immunity makes Cameroon vulnerable to new importations and spread of polioviruses.

Using search-constrained inverse distance weight modeling for near real-time riverine flood modeling: Harris County, Texas, USA before, during, and after Hurricane Harvey.

Flooding poses a serious public health hazard throughout the world. Flood modeling is an important tool for emergency preparedness and response, but some common methods require a high degree of expertise or may be unworkable due to poor data quality or data availability issues. The conceptually simple method of inverse distance weight modeling offers an alternative. Using stream gauges as inputs, this study interpolated stream elevation via inverse distance weight modeling under 15 different model input parameter scenarios for Harris County, Texas, USA, from August 25th to September 15th, 2017 (before, during, and after Hurricane Harvey inundated the county). A digital elevation model was used to identify areas where modeled stream elevation exceeded ground elevation, indicating flooding. Imagery and observed high water marks were used to validate the models' outputs. There was a high degree of agreement (between 79 and 88%) between imagery and model outputs of parameterizations visually validated. Quantitative validations based on high water marks were also positive, with a Nash-Sutcliffe efficiency of in excess of .6 for all parameterizations relative to a Nash-Sutcliffe efficiency of the benchmark of 0.56. Inverse distance weight modeling offers a simple, accurate method for first-order estimations of riverine flooding in near real-time using readily available data, and outputs are robust to some alterations to input parameters.

Achieving High Poliovirus Antibody Seroprevalence in Areas at Risk of Vaccine-Derived Poliovirus Transmission-Niger Experience.

BACKGROUND: Outbreaks of vaccine-derived poliovirus type 2 (VDPV2) continue to expand across Africa. We conducted a serological survey of polio antibodies in high-polio risk areas of Niger to assess risk of poliovirus outbreaks. METHODS: Children between 1 and 5 years of age were enrolled from structures randomly selected using satellite imaging enumeration in Diffa Province, Niger, in July 2019. After obtaining informed consent, dried blood spot cards were collected. Neutralizing antibodies against 3 poliovirus serotypes were detected using microneutralization assay at the Centers for Disease Control and Prevention. RESULTS: We obtained analyzable data from 309/322 (95.9%) enrolled children. Seroprevalence of polio antibodies was 290/309 (93.9%), 272/309 (88.0%), and 254/309 (82.2%) for serotypes 1, 2, and 3, respectively. For serotypes 1 and 2, the seroprevalence did not significantly change with age (P = .09 and P = .44, respectively); for serotype 3, it increased with age (from 65% in 1-2-year-olds to 91.1% in 4-5-year olds; P < .001). We did not identify any risk factors for type 2 seronegativity. CONCLUSIONS: With type 2 seroprevalence close to 90%, the risk of emergence of new cVDPV2 outbreaks in Niger is low; however, the risk of cVDPV2 importations from neighboring countries leading to local transmission persists. Niger should maintain its outbreak response readiness capacity and further strengthen its routine immunization.