this report was posted as an MMWR Early Release on the MMWR website (https://www.cdc.gov/mmwr).Transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is ongoing in many communities throughout the United States. Although case-based and syndromic surveillance are critical for monitoring the pandemic, these systems rely on persons obtaining testing or reporting a COVID-19-like illness. Using serologic tests to detect the presence of SARS-CoV-2 antibodies is an adjunctive strategy that estimates the prevalence of past infection in a population. During April 28-May 3, 2020, coinciding with the end of a statewide shelter-in-place order, CDC and the Georgia Department of Public Health conducted a serologic survey in DeKalb and Fulton counties in metropolitan Atlanta to estimate SARS-CoV-2 seroprevalence in the population. A two-stage cluster sampling design was used to randomly select 30 census blocks in each county, with a target of seven participating households per census block. Weighted estimates were calculated to account for the probability of selection and adjusted for age group, sex, and race/ethnicity. A total of 394 households and 696 persons participated and had a serology result; 19 (2.7%) of 696 persons had SARS-CoV-2 antibodies detected. The estimated weighted seroprevalence across these two metropolitan Atlanta counties was 2.5% (95% confidence interval [CI] = 1.4-4.5). Non-Hispanic black participants more commonly had SARS-CoV-2 antibodies than did participants of other racial/ethnic groups (p<0.01). Among persons with SARS-CoV-2 antibodies, 13 (weighted % = 49.9; 95% CI = 24.4-75.5) reported a COVID-19-compatible illness,* six (weighted % = 28.2; 95% CI = 11.9-53.3) sought medical care for a COVID-19-compatible illness, and five (weighted % = 15.7; 95% CI = 5.1-39.4) had been tested for SARS-CoV-2 infection, demonstrating that many of these infections would not have been identified through case-based

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BackgroundThe ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved.Methods and FindingsOver 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola (“cases”) were asked if they had exposure to other potential Ebola cases (“potential source contacts”) in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO’s response during the epidemic, and have been updated for publication.We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p < 0.001) between this proportion in a given district for a given month and the within-district transmission intensity, quantified by the estimated reproduction number (R). We also found a negative correlation (r = −0.37, p < 0.001) between R and the district proportion of hospitalised cases admitted within ≤4 days of symptom onset. These two proportions were not correlated, suggesting that reduced funeral attendance and faster hospitalisation independently influenced local transmission intensity. We were able to identify 14% of potential source contacts as cases in the case line-list. Linking cases to the contacts who potentially infected them provided information on the transmission network. This revealed a high degree of heterogeneity in inferred transmissions, with only 20% of cases accounting for at least 73% of new infections, a phenomenon often called super-spreading. Multivariable regression models allowed us to identify predictors of being named as a potential source contact. These were similar for funeral and non-funeral contacts: severe symptoms, death, non-hospitalisation, older age, and travelling prior to symptom onset. Non-funeral exposures were strongly peaked around the death of the contact. There was evidence that hospitalisation reduced but did not eliminate onward exposures. We found that Ebola treatment units were better than other health care facilities at preventing exposure from hospitalised and deceased individuals. The principal limitation of our analysis is limited data quality, with cases not being entered into the database, cases not reporting exposures, or data being entered incorrectly (especially dates, and possible mis...

In October 2012, a cluster of illnesses and deaths was reported in Uganda and was confirmed to be an outbreak of Marburg virus disease (MVD). Patients meeting the case criteria were interviewed using a standard investigation form, and blood specimens were tested for evidence of acute or recent Marburg virus infection by reverse transcription–polymerase chain reaction (RT-PCR) and antibody enzyme-linked immunosorbent assay. The total count of confirmed and probable MVD cases was 26, of which 15 (58%) were fatal. Four of 15 laboratory-confirmed cases (27%) were fatal. Case patients were located in 4 different districts in Uganda, although all chains of transmission originated in Ibanda District, and the earliest case detected had an onset in July 2012. No zoonotic exposures were identified. Symptoms significantly associated with being a MVD case included hiccups, anorexia, fatigue, vomiting, sore throat, and difficulty swallowing. Contact with a case patient and attending a funeral were also significantly associated with being a case. Average RT-PCR cycle threshold values for fatal cases during the acute phase of illness were significantly lower than those for nonfatal cases. Following the institution of contact tracing, active case surveillance, care of patients with isolation precautions, community mobilization, and rapid diagnostic testing, the outbreak was successfully contained 14 days after its initial detection.

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Here we describe clinicopathologic features of Ebola virus disease in pregnancy. One woman infected with Sudan virus in Gulu, Uganda, in 2000 had a stillbirth and survived, and another woman infected with Bundibugyo virus had a live birth with maternal and infant death in Isiro, the Democratic Republic of the Congo in 2012. Ebolavirus antigen was seen in the syncytiotrophoblast and placental maternal mononuclear cells by immunohistochemical analysis, and no antigen was seen in fetal placental stromal cells or fetal organs. In the Gulu case, ebolavirus antigen localized to malarial parasite pigment-laden macrophages. These data suggest that trophoblast infection may be a mechanism of transplacental ebolavirus transmission.

The Epi Info Viral Hemorrhagic Fever application (Epi Info VHF) was developed in response to challenges managing outbreak data during four 2012 filovirus outbreaks. Development goals included combining case and contact data in a relational database, facilitating data-driven contact tracing, and improving outbreak data consistency and use. The application was first deployed in Guinea, when the West Africa Ebola epidemic was detected, in March 2014, and has been used in 7 African countries and 2 US states. Epi Info VHF enabled reporting of compatible data from multiple countries, contributing to international Ebola knowledge. However, challenges were encountered in accommodating the epidemic's unexpectedly large magnitude, addressing country-specific needs within 1 software product, and using the application in settings with limited Internet access and information technology support. Use of Epi Info VHF in the West Africa Ebola epidemic highlighted the fundamental importance of good data management for effective outbreak response, regardless of the software used.