Coronavirus Cases Research Articles

Study On Mathematical Model For Forecasting of Novel Coronavirus in India

This paper reveals that the new coronavirus (Covid-19) is the biggest challenge for the whole world. The World Health Organization (WHO) has declared it an epidemic. The data was collected from 209 different individual WHO situation reports for COVID-19 in India. First, prognostic models were compared based on minimum AIC, MAPE, and MAE. Then the best forecasting models were used on the epidemiologic data of India to predict the epidemiologic pattern of prevalence. New and Total Covid-19 Deaths and Incidence ARIMA and SARIMA have been found suitable and predicted for September 1, 2020. Also, the prophet model is used for accuracy and prediction of the total number of coronavirus cases in India. The predicted values are checked against the past observed values so that the two values are very close. Using such time series models we can forecast for the next 15-20 days and plan accordingly. This kind of projection helps in planning for the future.

Admission and outcomes of COVID-19 among chronic obstructive pulmonary diseases patients in Africa: protocol for a systematic review and meta-analysis.

When the coronavirus case was originally reported in Wuhan, China, in December 2019, it quickly spread throughout the world and became a global public health problem. Evidence of the admission and outcomes of coronavirus disease among patients with chronic obstructive pulmonary disease (COPD) has not been reported in Africa. Consequently, this research protocol uses a systematic review and meta-analysis of the admission and outcomes of COVID-19 in patients with COPD in Africa. All observational studies published in the English language and reporting on the prevalence, admission and outcomes of COVID-19 among patients with COPD in Africa will be included. A search strategy will be implemented using electronic databases and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocol recommendations. The findings of this review will be reported to health program designers, decision-makers and healthcare providers.

Impact of risk information on health beliefs and behaviours: experimental evidence

ABSTRACT The local prevalence of infections and the severity of their consequences are among the key determinants of the adoption of preventive behaviours against infectious diseases. In Bangladesh, where local COVID-19 infection statistics were not readily accessible, I found that most people underestimated the local prevalence of COVID-19 infections while overestimated the fatality rate. In a randomized experiment, the treatment group was provided with information about the coronavirus case numbers in their districts, along with the case fatality rate in Bangladesh and globally. Immediately after receiving this information, the treatment group perceived a higher infection risk. Nine to fifteen days post-intervention, those who received information were less likely to underestimate the local prevalence and, consequently, still perceived a higher infection risk. They also updated their belief about the fatality rate downward. Potentially due to this countervailing update of risk beliefs, the information had no effect on self-reported preventive behaviours.

Prevalence of SARS-CoV-2 and Associated Factors Among Individuals During the Second Wave of Infection in Yaounde, Cameroon

Background: The first case of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was reported in Cameroon on March 5, 2020, in Yaounde. Since then, viral propagation occurred nationwide with a higher burden in urban settings and limited evidence on transmission patterns. Our objective was to determine the prevalence of SARS-CoV-2 and associated factors among individuals in urban settings of Cameroon during the second wave of the pandemic. Methods: a population-based cross-sectional survey was conducted from January 18 to April 26, 2021, among 807 consenting individuals living in four major districts of the city of Yaoundé. Out of 711 nasopharyngeal swabs collected, 671 tests were performed by SARS-CoV-2 ribonucleic acid (RNA) extraction and real time-polymerase chain reaction (RT-PCR) at the National Public Health Laboratory (NPHL). Data were analysed using SPSS v21.0, with p < 0.05 considered statistically significant. Results: overall SARS-CoV-2 RNA positivity rate was 11.62% (78/671), without any significant difference between males and females with 10.9% versus 12.20% respectively (OR = 0.87, p = 0.5). However, clinical status was associated with SARS-CoV-2 RNA positivity rate in symptomatic (26.3%) vs. asymptomatic (9.7%) individuals (Odds Ratio [OR] = 3.30, p = 0.0001). Other factors associated with SARS-CoV-2 RNA positivity include exposure to more than 15min with confirmed cases, wearing a non-conventional facemask (p ˂ 0.05), the practice of occasional hand hygiene before and after contact with patients (OR = 3.60, p < 0.0001) or always as recommended (OR = 0.28, p = 0.0001), hand hygiene before and after occasional contact with objects (OR = 2.05, p = 0.0043) or always as recommended (OR = 0.48, p = 0.0027), surgical mask (OR = 0.17, p = 0.013) and standard mask (p = 0.026). Conclusion: From this urban setting of Cameroon, coronavirus disease 2019 (COVID-19) reached an alert burden (10-20%) during the second wave of the pandemic at the district level. Interestingly, SARS-CoV-2 infection was driven by the presence of symptoms, close contact with confirmed cases, limited adherence to recommended barrier measures, and the use of non-conventional facemasks.

Regression model - Correlation Between Age and Coronavirus Cases in Australia

Regression model - Correlation Between Age and Coronavirus Cases in Australia

Mortality Predictors among COVID-19 Patients Admitted to the Intensive Care Unit in Sulaymaniyah City, Iraq: A Cross-Sectional Study

Background and objectives: On February 22, 2020, Iraq confirmed the first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The number of confirmed cases in Sulaimaniyah city reached 152,846, with 3,178 deaths. Our objective was to assess the clinical characteristics and identify variables that may predict mortality of COVID-19 cases in the intensive care unit. Methods: Our cross-sectional study involved patients who were diagnosed with laboratory-confirmed Covid-19 and admitted to the intensive care unit of Shaheed Dr. Hemn Hospital/ Sulaymaniyah/ Iraqi Kurdistan from January 15, 2021, to April 29, 2022. Collected data included the patient's demographic characteristics, vital signs, hematological parameters, and management as documented in the patient's medical records. Patients were separated to two groups: those who were discharged and those who were dead. The binary logistic regression model was used to identify independent factors that predict mortality. Results: During the study period, 403 patients were admitted to the intensive care unit. Case fatality rate was high 69.2%. The patient’s average age was 61.42 ±13.17 years, and 249 (61.8%) of them were men. The majority of the patients 319 (79.2%) had comorbidities. Lymphocytopenia was observed in 306 (75.9%) of the patients and there was a statistically significant difference between discharged and death groups (p value 0.021). Remdesivir was given to 185 patients (45.9%) and there was a statistically significant difference between discharged and death groups, p-value <0.001. Conclusions: Critically ill patients with COVID-19 had a high mortality rate in the intensive care unit. low SBP, GCS<15, high D. dimer, and poor oxygen saturation were considered predictor factors of mortality.

Acute coronary syndromes did not decrease during COVID-19 pandemic in an isolated Greek population.

Introduction: There are worldwide reports that hospital admissions for acute coronary syndrome (ACS) have declined during the COVID-19 pandemic. Chios is a Greek island with only one confirmed coronavirus case during the lockdown. This study aimed to compare admissions for ACS in Chios General Hospital, Greece, between the COVID-19 lockdown period and the same period in the previous year. Methods: Retrospective record analysis of an isolated insular population referring to the only district hospital on the island. ACS incidence, type, and complications were recorded and compared between 26/02/2020-04/05/2020 and between 26/02/2019-04/05/2019. Results: ACS hospital admissions in 2020 were 1.72/10,000 inhabitants vs. 1.03/10,000 inhabitants in 2019 (p = 0.317). No differences in ACS type, duration from symptom onset to first medical contact, hemodynamic status, left ventricular function at discharge, or complications were recorded. Conclusion: The incidence of ACS did not decrease and the prognosis was not worse during the COVID-19 pandemic in a strictly isolated Greek insular population not overwhelmed by coronavirus cases.

Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques

Coronavirus has long been considered a global epidemic. It caused the deaths of nearly 7.01 million individuals and caused an economic downturn. The number of verified coronavirus cases is increasing daily, putting the whole human race at danger and putting strain on medical experts to eradicate the disease as rapidly as possible. As a consequence, it is vital to predict the upcoming coronavirus positive patients in order to plan actions in the future. Furthermore, it has been discovered all across the globe that asymptomatic coronavirus patients play a significant part in the disease’s transmission. This prompted us to incorporate similar examples in order to accurately forecast trends. A typical strategy for analysing the rate of pandemic infection is to use time-series forecasting technique. This would assist us in developing better decision support systems. To anticipate COVID-19 active cases for a few countries, we recommended a hybrid model utilizing a fuzzy time series (FTS) model mixed with a non-linear growth model. The coronavirus positive case outbreak has been evaluated for Italy, Brazil, India, Germany, Pakistan, and Myanmar through June 5, 2020 in phase-1, and January 15, 2022 in phase-2, and forecasts active cases for the next 26 and 14 days respectively. The proposed framework fitting effect outperforms individual logistic growth and the fuzzy time series techniques, with R-scores of 0.9992 in phase-1 and 0.9784 in phase-2. The proposed model provided in this article may be utilised to comprehend a country’s epidemic pattern and assist the government in developing better effective interventions.

Pregnancy and COVID—Complications and its Challenges: A Case Series from a Tertiary Care Center in the Northeastern Part of India

The objective was to describe the course of management of all the coronavirus disease 2019 (COVID-19)-positive pregnant women, admitted to a 150-bedded tertiary care center in Guwahati, Assam, from March 01, 2021, till June 15, 2021. We collected a case series of 13 COVID-19-positive pregnant women, mostly in their second and third trimesters, admitted to our center between March 01, 2021, and June 15, 2021. All of the cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection were confirmed by a positive result on real-time reverse transcriptase-polymerase chain reaction (RT-PCR) tests. We aimed to show the course of management and maternal outcomes in these cases. The Results show that among the 13 infected pregnant women, three presented with severe SARS-CoV-2 pneumonia and were treated in the intensive care unit (ICU) and the rest were treated in wards as the mild category and discharged successfully. Two among them required invasive mechanical ventilation (IMV), and one was managed on non-invasive ventilation (NIV). Of the two mechanically ventilated patients, one succumbed to the illness and the other patient was successfully weaned in 5 days and shifted out after 15 days of the ICU stay. The third patient was on NIV support for 7 days, weaned off to low-flow oxygen therapy via face mask/ nasal prongs, and shifted out after 17 days of the ICU stay. Data on pregnant women with coronavirus infection are scarce and contradictory as the effect of SARS-CoV-2 on pregnancy is not clear. It is highly important to systemically investigate the clinical characteristics and outcomes of pregnant women with COVID-19 to provide strong guidelines for prevention, treatment, and management.

코로나 이환 경험자를 대상으로 주관적 구강상태 인지와 미각 역치 간의 상호관계 분석

Objectives: The purpose of this study was to confirm the analysis of taste threshold and evaluate the relationship between taste threshold and changes in taste according to oral factors and COVID-19 disease. Methods: In this study, a total of 203 female college students participated. Taste thresholds for five tastes were measured. Information on subjective oral environment and potential COVID-19 effects on taste thresholds was gathered through a questionnaire. Results: Sweetness stood out with the highest sensitivity at 4.19 among the five basic tastes. Subjects occasionally experiencing subjective bad breath showed significant sensitivity to sour taste (sometimes 3.12, never 2.47). Individuals frequently perceiving a subjective dry mouth exhibited heightened sensitivity to bitter taste, scoring 4.13, significantly higher than the 3.38 observed when there was no dryness. While taste thresholds did not significantly differ for those with confirmed coronavirus cases, there was a general tendency for elevated thresholds across all tastes. Subjects with altered smell during COVID-19 diagnosis were notably more sensitive to sour tastes, with a statistically significant difference. Changes in taste and smell correlated positively, with most correlations showing significant positive associations. Conclusions: This study COVID-19 infection led to taste and smell changes, but post-recovery taste thresholds remained unaffected. These findings contribute to understanding the epidemiological aspects of taste and serve as foundational data for exploring taste changes, significant as oral-related clinical symptoms of COVID-19, a classified class 4 infectious disease.

Exploring COVID-19 case fatality rate trends in the UnionTerritory of Jammu & Kashmir, India: A comprehensive study

Background: In 2021, India witnessed a resurgence in Coronavirus cases, marking the onset of the second wave of the pandemic, six months after the first wave peaked in September 2020. This study aims to investigate trends and disparities in COVID-19 case-fatality rates across various districts within the Union territory of Kashmir Division during the period of escalating cases after the decline of the initial wave. Methods: A cross-sectional study was conducted in a region of the Union territory of Jammu & Kashmir, India. After obtaining necessary permissions, data collection took spanned from November 1, 2020, to May 8, 2021. Aggregated weekly data from various zones in India was collected during the study period and exploratory analysis was conducted. Results: The data analysis reveals that, from November 2020 to May 2021, COVID-19 cases in the Union Territory of Jammu & Kashmir demonstrated fluctuating tendencies. Notably, there was a substantial surge in March 2020, especially in Srinagar, which suggests the possibility of localised breakouts or dynamics of transmission. Even though Rapid Antigen Test (RAT) positivity rates were originally low in all districts, by the end of the study period, they had significantly increased, particularly in Srinagar and Shopian. This suggests that the dynamics of virus transmission may have changed, either as a result of new variations or behavioural changes. Conclusion: Our findings highlight those disparities in case-fatality rates between districts, whether low or high, are significantly influenced by the number of confirmed cases. Case-fatality rates were highly impacted by variations in the testing accessibility, containment strategies, and healthcare infrastructure. The results highlight how crucial it is to allocate resources fairly and implement focused interventions in order to lessen the pandemic's effects and safeguard the region's public health.

Impact of information epidemic in the COVID-19 pandemic

In December 2019, the first case of Corona virus was identified in Wuhan, China. On March 11th, 2020, the World Health Organization (WHO) declared COVID-19 a pandemic due to a rapid surge in case numbers. This global health crisis has been accompanied by a significant amount of misleading and false information about COVID-19, creating uncertainty among the public as they seek trustworthy and reliable sources. The impact of misinformation extends beyond information sharing it can fundamentally distort common public perceptions of the risks associated with the virus. The unregulated dissemination of both accurate and inaccurate information on social media further contributes to the confusion, transforming a pandemic into an infodemic.

High SARS-CoV-2 attack rates in areas with low detection after community transmission established in Port Vila, Vanuatu, April 2022.

On 4 March 2022, the first community-acquired case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was reported in Vanuatu, with community transmission occurring subsequently. It was expected that the number of notified SARS-CoV-2 cases would be an underestimate of the true infection rate of this outbreak; however, the magnitude of underreporting was unknown. The purpose of this study was to provide a population-based estimate of SARS-CoV-2 infection shortly after the first reports of community transmission, to understand the level of underdetection and undernotification in Vanuatu and thus to inform ongoing prevention and response activities. We conducted a cross-sectional SARS-CoV-2 prevalence study in two geographical administrative areas in Port Vila, Vanuatu in April 2022. All residents in selected areas were eligible. Trained teams conducted demographic and behavioural interviews and collected nasal specimens. Specimens were tested by polymerase chain reaction. The primary outcomes were the rates of SARS-CoV-2 attack (point prevalence) and cumulative attack, underdetection, notification and household secondary attack. A total of 252 people from 84 households participated. Among 175 people who had a sample collected, 91 were SARS-CoV-2-positive (attack rate 52.0%). Most cases had not been detected before the study (underdetection rate 91.5%). More than half of previously detected cases were notified (notification rate 65.2%). Within the first few weeks of community transmission, more than half of participants in the selected areas had evidence of SARS-CoV-2 infection; however, most infections had been undetected. This study provides important information about the rapid spread of novel infectious diseases in Vanuatu.

Impact of coronavirus pandemic on stock index: A polynomial regression with time delay

MotivationUnder contemporary market conditions in China, the stock index has been volatile and highly reflect trends in the coronavirus pandemic, but rare scientific research has been conducted to model the possible nonlinear relations between the two indicators. Added, on the advent that covid-related news in one time period impacts the stock market in another period, time delay can be an equally good predictor of the stock index but rarely investigated. ObjectivesTo contribute to filling the gaps identified in existing research, this study models relationship between the stock market index and coronavirus pandemic by leveraging volatility in the stock market and covid data through time delay and best degree in a polynomial environment. The resultant optimal time delay and best degree model is used to derive a high-accuracy prediction of stock market index. NoveltyIn line with the possible relations, the novelty of this study is that it proposes, validates and implements polynomial regression with time delay to model nonlinear relationship between the stock index and covid. MethodsThis study utilizes high-frequency data from January 2020 to the first week of July 2022 to model the nonlinear relationship between the stock index, new covid cases and time delay under polynomial regression environment. FindingsThe empirical results show that time delay and new covid cases, when modelled in a polynomial environment with optimal degree and delay, do present better representation of the nonlinear relationship such predictors have with stock index for China. Relative to results from the polynomial regression without delay, the empirical evidence from the model with delay show that an optimal time delay of 17 weeks makes it possible to predict the stock index at high accuracy and record improvements of 16-fold or higher. The representative delay model is used to project for up to 17 weeks for future trends in the stock index. ImplicationThe implication of the findings herein is that the prowess of the time delay polynomial regression is heavily dependent on instability in covid-related time trends and that researchers and decision-makers should consider modeling to cover for the unsteadiness in coronavirus cases to achieve better results.

Accurate detection of coronavirus cases using deep learning with attention mechanism and genetic algorithm

The novel coronavirus disease has caused severe threats to the daily life and health of people all over the world. Hence, early detection and timely treatment of this disease are significant to prevent the coronavirus's spread and ensure more effective patient care. This work adopted an integrated framework comprising deep learning and attention mechanism to provide a more effective and reliable diagnosis. This framework consists of two convolution neural network (CNN), a bidirectional LSTM, two fully-connected layers (FCL), and an attention mechanism. The main aim of the proposed framework is to reveal a promising approach based on deep learning for early and timely detection of coronavirus disease. For greater accuracy, the framework's hyperparameters are tuned by means of a genetic algorithm. The effectiveness of the proposed framework has been examined utilizing a public dataset including 18 different blood findings from Albert Einstein Israelita Hospital in Sao Paulo, Brazil. Additionally, within the experimental studies, the proposed framework is subjected to comparison with the state-of-the-art techniques, evaluated across various metrics. Based on the derived consequences, the proposed framework has yielded enhancements in accuracy, recall, precision, and F1-score, registering approximate improvements of 1.27%, 4.07%, 3.20%, and 2.88%, respectively, as measured against the second-best rates.

Covid-19 Drug Control Based on BPOM Informatorium with ABC Method at the Pharmacies of Bandung City

According to statistical data from JHU CSSE COVID-19 in June 2021, there was an increase in positive cases of Coronavirus and began to decline at the end of August 2021. The planning that has been made must be corrected using the ABC method because a type of drug can consume a large budget if it is used a lot and is expensive. The purpose of this ABC value analysis is to identify the types of drugs from the scale of use that require the most costs. This type of non-experimental research uses descriptive methods through retrospective quantitative data collection using previous data, namely June – August 2021. Based on the scale of the use of Covid-19 drugs which are included in group A with the highest number of uses, it is necessary to ensure the availability of sufficient stock for avoid stock shortages that can hamper patient care in hospitals and can cause losses to hospitals. Drugs that are included in group B are adequately controlled by using warehouse stock cards and stock cards in the compounding and retail sales rooms. Drug group C needs to be controlled so that the number of drugs is not too excessive to avoid losses due to expired or damaged drugs. A review of the ABC analysis can be carried out annually along with the determination of the use of a standard drug list and the preparation of an annual budget plan.

Psychological Approach Physical Distancing Model on Immune Resistance Imun New Normal Post Pandemic COVID 19

BACKGROUND: The first COVID-19 reported in Indonesia on March 2, 2020, was two cases. Data on March 31, 2020, showed that there were 1528 confirmed cases and 136 deaths. The COVID-19 mortality rate in Indonesia is 8.9%, this figure is the highest in Southeast Asia. As of March 30, 2020, there were 693,224 cases and 33,106 deaths worldwide. Inadequate immune response leads to viral replication and tissue damage. On the other hand, an exaggerated immune response can cause tissue damage. Several other risk factors established by the Centers for Disease Control and Prevention are close contact, including living in the same house as a COVID-19 patient and a history of travel to an infected area. Based on a preliminary study at the Yogyakarta Special Region Health Center and the Borobudur Magelang Health Center, Central Java, there were 95 cases of corona virus and 75% with comorbidities. Of the corona cases in the Special Region of Yogyakarta and in Borobudur, Magelang, Central Java, the body’s resistance decreased. Researchers are interested in taking the title “The Effect of Psychological Approach Physical Distancing on New Normal Immune Resistance after the COVID 19 Pandemic” AIM: It is known the influence of Psychological Approach Physical Distancing on New Normal Immune Resistance after the COVID 19 Pandemic. METHODS: This type of research is Research and Development using a quasi-experimental, “Pre-test Post-test with Control Group Design” design. In this design, there was a comparison group (control), the observations were carried out twice. The first observation was to determine the New Normal maximum resilience after the COVID 19 pandemic before being given Psychological Approach Physical Distancing and the second observation after being given Psychological Approach Physical Distancing with the criteria for comorbid respondents at the Puskesmas. Data were analyzed using pair t-test and Wilcoxon with a significant level of p < 0.05. RESULTS: Differences in initial immune resistance at month 1, month 1 and month 2, and month 2 and month 3 there was a difference with p = 0.000 (< 0.05). CONCLUSION: There is an influence of Psychological Approach Physical Distancing on New Normal Immune Resistance after the COVID 19 Pandemic.

Sentiment analysis of the COVID-19 vaccine perception.

The sharp rise in coronavirus cases in the United States, as well as other countries, is driven by variants such as the Omicron substrain, BA4 and BA5. Keeping up to date with COVID-19 vaccination and wearing masks are essential tools for mitigating the pandemic. Social media plays a vital role in sharing and exchanging information, but it also affects perceptions of social phenomena. In this study, we conducted sentiment analysis and topic modeling to investigate vaccine perception using 338,465 COVID-19 vaccine-related comments collected from January 2020 to May 2021 on Reddit. This study stands apart from prior COVID-related research on social media, particularly on Reddit, as it conducted separate analyses for each COVID vaccine and examines public sentiment with various societal events, including vaccine development progress and government responses to COVID. The findings reveal two notable spikes in the number of comments containing the keyword "vaccine". This suggests that discussions about vaccines tend to increase during times of significant social and political events, indicating that people's attention and interest in the topic are influenced by current events. Understanding the public perception of vaccines and identifying factors influencing vaccine perception could help propose appropriate interventions to promote vaccination.

Analyzing knowledge, attitudes, and practices towards COVID-19: The second cross-sectional survey in the Cabo Verdean population

ContextThe emergence and increase of cases of the new coronavirus in Cabo Verde led to the implementation of several infection control measures. However, the success of these measures depends largely on the population's knowledge about the disease and their commitment to fighting it. This study aimed to analyze the knowledge, attitudes, perceptions, and practices of the resident Cabo Verdean population regarding COVID-19. MethodologyA cross-sectional study was carried out among 2754 participants aged 12 and above. The data collection instrument, a questionnaire, was applied in the field by trained surveyors. Descriptive statistics, chi-square tests, Spearman correlation, and simple and multiple regression analyses were performed to assess the sample's determinants of knowledge, attitudes, and practices towards COVID-19. ResultsOf the 2754 study participants, 1331 (48.3%) were male. The mean age of the respondents was 28.5 years (SD = 14.1). The overall knowledge rate was 79%. Logistic regression analysis showed that knowledge was significantly associated with secondary education or higher (p < 0.001), marital status married or de facto partnership (p < 0.001), and residence in the urban area of São Vicente (p = 0.025). The rate of correct answers concerning attitudes was 75.7%, with the majority of respondents agreeing with the measures implemented by the authorities in managing the pandemic (n = 2272; 82.9%). The results show considerable hesitation concerning the vaccine, which is significantly associated with the age group 18 to 30 (p < 0.001). Practices obtained the lowest scores, and physical distancing was one of the preventive measures least complied with. Binary logistic regression revealed a significant association between sex, age, education, and marital status with practices. Knowledge (rho = 0.1436; p < 0.001) and attitudes (rho = 0.1068; p < 0.001) were significantly associated with practices. ConclusionFinancial difficulties seem to hinder compliance with infection control measures. Public health policies may have greater adherence if accompanied by social and economic support strategies. Studies investigating the behavior of infected persons during the infection period may provide helpful information in understanding the mechanisms underlying the virus transmission in the country.

Severe acute respiratory syndrome coronavirus 2-reactive salivary antibody detection in South Carolina emergency healthcare workers, September 2019-March 2020.

On 19 January 2020, the first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was identified in the United States, with the first cases in South Carolina confirmed on 06 March 2020. Due to initial limited testing capabilities and potential for asymptomatic transmission, it is possible that SARS-CoV-2 may have been present earlier than previously thought, while the immune status of at-risk populations was unknown. Saliva from 55 South Carolina emergency healthcare workers (EHCWs) was collected from September 2019 to March 2020, pre- and post-healthcare shifts, and stored frozen. To determine the presence of SARS-CoV-2-reactive antibodies, saliva-acquired post-shift was analysed by enzyme-linked immunosorbent assay (ELISA) with a repeat of positive or inconclusive results and follow-up testing of pre-shift samples. Two participants were positive for SARS-CoV-2 N/S1-reactive IgG, confirmed by follow-up testing, with S1 receptor binding domain (RBD)-specific IgG present in one individual. Positive samples were collected from medical students working in emergency medical services (EMSs) in October or November 2019. The presence of detectable anti-SARS-CoV-2 antibodies in 2019 suggests that immune responses to the virus existed in South Carolina, and the United States, in a small percentage of EHCWs prior to the earliest documented coronavirus disease 2019 (COVID-19) cases. These findings suggest the feasibility of saliva as a noninvasive tool for surveillance of emerging outbreaks, and EHCWs represent a high-risk population that should be the focus of infectious disease surveillance.