Active COVID-19 Cases Research Articles

Characterization of a Covid-fired urban bike delivery system: The Montreal experience

The Covid-19 pandemic led to a rapid change in consumers' demand for home deliveries and traditional delivery modes were unable to deliver consumers and shops due to the sudden increase in demand. This study investigates the results of a local cargo bike delivery initiative in Montreal, Canada, during the forced closure of non-essential businesses from March to May 2020. The objective of this study is to identify the variables which interacted with the performance of a cargo bike delivery initiative while considering the specific context of the Covid-19 pandemic. To assess such changing conditions, a multilevel linear regression analysis is carried out. This analysis allows to quantify the number of home deliveries while considering the impact of the number of active Covid-19 cases and sociodemographic predictors on the cargo bike initiative. The dataset covers 6,700 deliveries made by cargo bikes in the city of Montreal over 16 weeks. The results show that the presence of advertising and the characteristics of consumers' households, such as a smaller household size and a younger average age are amongst the strongest factors for the success of such initiative. The specific context of the Covid-19 pandemic allowed a better penetration of cargo bikes within the last-mile ecosystem.

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.

Wastewater surveillance of severe acute respiratory syndrome coronavirus-2 in open drains of two Indian megacities captures evolutionary lineage transitions: a zonation approach.

Wastewater-based environmental surveillance (WBES) has been proven as proxy tool for monitoring nucleic acids of pathogens shed by infected population before clinical outcomes. The poor sewershed network of low to middle-income countries (LMICs) leads to most of the wastewater flow through open drains. We studied the effectiveness of WBES using open drain samples to monitor the emergence of the SARS-CoV-2 variants in 2 megacities of India having dense population through zonation approach. Samples from 28 locations spanned into 5 zones of Pune region, Maharashtra, India, were collected on a weekly basis during October 2021 to July 2022. Out of 1115 total processed samples, 303 (~ 27%) tested positive for SARS-CoV-2. The periodical rise and fall in the percentage positivity of the samples was found to be in sync with the abundance of SARS-CoV-2 RNA and the reported COVID-19 active cases for Pune city. Sequencing of the RNA obtained from wastewater samples confirmed the presence of SARS-CoV-2. Of 337 sequences, lineage identification for 242 samples revealed 265 distinct SARS-CoV-2 variants including 10 highly transmissible ones. Importantly, transition from Delta to Omicron variant could be detected in wastewater samples 2weeks prior to any clinically reported Omicron cases in India. Thus, this study demonstrates the usefulness of open drain samples for real-time monitoring of a viral pathogen's evolutionary dynamics and could be implemented in LMICs.

Can COVID-19 herd immunity be achieved at a city level?

We propose a new approach to estimate the vaccination rates required to achieve herd immunity against SARS-COV2 virus at a city level. Based on information obtained from the Israeli Ministry of Health, we estimate two separate quadratic models, one for each dose of the BNT162b2 mRNA Pfizer vaccine. The dependent variable is the scope of morbidity, expressed as the number of cases per 10,000 persons. The independent variables are the first and second vaccination rates and their squares. The outcomes corroborate that herd immunity is achieved in the case that 71 percent of the urban population is vaccinated, and the minimum anticipated scope of morbidity is approximately 5 active COVID-19 cases per 10,000 persons, compared to 53-67 cases per 10,000 persons for zero vaccination rate. Findings emphasize the importance of vaccinations and demonstrate that urban herd immunity may be defined as a situation in which people continue to interact, yet the COVID-19 spread is contained. This, in turn, might prevent the need for lockdowns or other limitations at the city level.

To determine the role of transcutaneous retrobulbar amphotericin B in COVID-19-related rhino-orbital-cerebral mucormycosis.

The purpose of this study was to determine the role of transcutaneous retrobulbar amphotericin B (TRAMB) injection in COVID-19-related rhino-orbital-cerebral mucormycosis (ROCM). This prospective interventional case series included 7 eyes of 7 patients diagnosed with COVID-19 ROCM presenting from May to September 2021 at a tertiary care center in North India. Active or recovered cases of COVID-19 with clinical features suggestive of ROCM and early orbital involvement were included. Following functional endoscopic sinus surgery, patients were given TRAMB 3.5 mg/ml in single ocular quadrant depending upon the muscle involved on contrast-enhanced magnetic resonance imaging (MRI). Patients were followed up for a period of 12 weeks. All patients were diabetic and COVID-19 reverse transcription-polymerase chain reaction positive. The mean age of patients was 48.4 years ± 11 years, out of which 5 patients were male and 2 were female. The left eye was involved in 57.14% of cases whereas 42.85% of cases had right eye involvement. Presenting complaints were drooping of the upper lid (57.14%; n = 4), proptosis (28.57%; n = 2), facial pain and swelling (85.71%; n = 6), and nasal block (57.14%; n = 4). Stagewise distribution was 3a in four patients (57.14%), 3b in two patients (28.57%), and 4a in one patient. TRAMB injections (median - 3) were given, and improvement was seen in all patients in terms of ocular movements, ptosis, proptosis, and muscle enhancement on MRI scan. Three patients had an increase in intraocular pressure and inflammation which settled within a week with favorable anatomical and functional outcomes. Transcutaneous retrobulbar amphotericin B should be considered an adjunctive treatment modality for ROCM patients with limited orbital disease to preserve ocular function.

The effects of the COVID-19 pandemic period on stock market return and volatility. Evidence from the Pakistan Stock Exchange.

The COVID-19 pandemic has emerged as a significant event of the current century, introducing substantial transformations in economic and social activities worldwide. The primary objective of this study is to investigate the relationship between daily COVID-19 cases and Pakistan stock market (PSX) return volatility. To assess the relationship between daily COVID-19 cases and the PSX return volatility, we collected secondary data from the World Health Organization (WHO) and the PSX website, specifically focusing on the PSX 100 index, spanning from March 15, 2020, to March 31, 2021. We used the GARCH family models for measuring the volatility and the COVID-19 impact on the stock market performance. Our E-GARCH findings show that there is long-term persistence in the return volatility of the stock market of Pakistan in the period of the COVID-19 timeline because ARCH alpha (ω1) and GARCH beta (ω2) are significant. Moreover, is asymmetrical effect is found in the stock market of Pakistan during the COVID-19 period due to Gamma (ѱ) being significant for PSX. Our DCC-GARCH results show that the COVID-19 active cases have a long-term spillover impact on the Pakistan stock market. Therefore, the need of strong planning and alternative platform should be needed in the distress period to promote the stock market and investor should advised to make diversified international portfolio by investing in high and low volatility stock market to save their income. This study advocated the implications for investors to invest in low volatility stock especially during the period of pandemics to protect their return on investment. Moreover, policy makers and the regulators can make effective policies to maintain financial stability during pandemics that is very important for the country's economic development.

Analisis Social Influence, Perceived Ease of Use terhadap Behavioural Intention dengan Mediasi Perceived Usefulness

The emergence of Covid-19 pandemic has spread widely to various countries, which of one includes Indonesia. Where in 2020, Indonesia also impact on this pandemic, so the government took action on PPKM policy regulations by recommending all activities carried outside such as work, carried out at home to reduce circulation wider spread of the virus. After going through a situation where people must comply with health protocols and maintain cleanliness. Where at the end of 2022, the Ministry of Health announced that active cases of Covid-19 had decreased consistently and gave optimism to the current efforts to deal with Covid-19, of course it has made people accumulate an extraordinary willingness to travel. Due to the emergence of people's behavioral intentions to travel by ordering their travel activities on online booking service provider applications, where these applications can provide convenience and useful for them so that does not require excessive effort also can be used easily and efficiently. Indirectly it can also make the tourism industry continue to experience the most significant growth in searching for online travel agents because people's behavioral intentions are influenced by social influences that can provide convenience and benefits when using online booking service provider applications.

COVID-19 trends at the University of Tennessee: predictive insights from raw sewage SARS-CoV-2 detection and evaluation and PMMoV as an indicator for human waste.

Wastewater-based epidemiology (WBE) has become a valuable tool for monitoring the prevalence of SARS-CoV-2 on university campuses. However, concerns about effectiveness of raw sewage as a COVID-19 early warning system still exist, and it's not clear how useful normalization by simultaneous comparison of Pepper Mild Mottle Virus (PMMoV) is in addressing variations resulting from fecal discharge dilution. This study aims to contribute insights into these aspects by conducting an academic-year field trial at the student residences on the University of Tennessee, Knoxville campus, raw sewage. This was done to investigate the correlations between SARS-CoV-2 RNA load, both with and without PMMoV normalization, and various parameters, including active COVID-19 cases, self-isolations, and their combination among all student residents. Significant positive correlations between SARS-CoV-2 RNA load a week prior, during the monitoring week, and the subsequent week with active cases. Despite these correlations, normalization by PMMoV does not enhance these associations. These findings suggest the potential utility of SARS-CoV-2 RNA load as an early warning indicator and provide valuable insights into the application and limitations of WBE for COVID-19 surveillance specifically within the context of raw sewage on university campuses.

Application of wastewater-based epidemiology for monitoring COVID-19 in hospital and housing wastewaters

An alternative and complementary diagnostic method of surveillance is provided by wastewater-based surveillance (WBS), particularly in low-income nations like Nepal with scant wastewater treatment facilities and clinical testing infrastructure. In this study, a total of 146 water samples collected from two hospitals (n = 63) and three housing wastewaters (n = 83) from the Kathmandu Valley over the period of March 2021–Febraury 2022 were investigated for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using quantitative reverse transcription TaqMan PCR assays targeting the N and E genes. Of the total, 67 % (98/146) samples were positive for SARS-CoV-2 RNA either by using N- or E-gene assay, with concentrations ranging from 3.6 to 9.1 log10 copies/L. There was a significant difference found between positive ratio (Chi-square test, p < 0.05) and concentration (t-test, p = 0.009) of SARS-CoV-2 RNA detected from hospital wastewater and housing waters. Wastewater data are correlated with COVID-19 active cases, indicating significance in specific areas like the Hospital (APFH) (p < 0.05). According to the application of a bivariate linear regression model (p < 0.05), the concentrations of N gene may be used to predict the COVID-19 cases in the APFH. Remarkably, SARS-CoV-2 RNA was detected prior to, during, and following clinical case surges, implying that wastewater surveillance could serve as an early warning system for public health decisions. The significance of WBS in tracking and managing pandemics is emphasized by this study, especially in resource-constrained settings.

Centralized CNN–GRU Model by Federated Learning for COVID-19 Prediction in India

In 2019, the corona virus was found in Wuhan, China. The corona virus has traveled several countries in the world from the beginning of 2020. The early estimation of COVID-19 cases is one of the efficient approaches to control the pandemic. Many researchers had proposed the deep learning model for the efficient estimation of COVID-19 cases for different provinces in the world. The research work had not focused on the discussion of robustness in the model. In this study, centralized federated-convolutional neural network–gated recurrent unit (Fed-CNN–GRU) model is proposed for the estimation of active cases per day in different provinces of India. In India, the uneven transmission of COVID-19 virus was seen in 36 provinces due to the different geographical areas and population densities. So, the methodology of this study had focused on the development of single deep learning algorithm, which is robust and reliable to estimate the active cases of COVID-19 in different provinces of India. The concept of transfer and federated learning is involved to enhance the estimation of active cases of COVID-19 by the CNN–GRU model. The study had considered the active cases per day dataset for 36 provinces in India from 12 March, 2020 to 17 January, 2022. Based on the study, it is proven that the centralized CNN–GRU model by federated learning had captured the transmission dynamics of COVID-19 in different provinces with an enhanced result.

Hotels in France During and Post COVID-19: A Change Management Case Study

COVID had strongly impacted the tourism sector. According to the ministry of Europe and foreign affairs, the sector saw a decline of almost 50% in international tourism revenues generated in the first 7 months of 2020. The restrictions imposed on travel and closure of borders to reduce the number of active COVID-19 cases caused several hotels to be closed or rethink their management methods to survive. The aim of this case study is first, to understand the impact of this health crisis in France and to list the different strategies used in the literature review to overcome COVID-19 impact. Second, the study focuses on Accor group in France, the dilemma they faced, and the different strategies put in place to bounce back from the crisis and support change. This study demonstrates that alternative strategies and change processes can be used in times of crisis to survive and remain competitive.

The influence of air pollutants on COVID-19 severity in the administrative and industrial city of Kharkiv

Background. Despite the official statement by the World Health Organization regarding the end of coronavirus disease (COVID-19) pandemic, the risk of an epidemic rise in morbidity remains due to the active circulation of the pathogen and its mutation. Social, natural, environmental, and other factors can contribute to the spread of COVID-19. Air pollutants are extremely dangerous for humans, and a mixture of aerosols and dust particles in the air can serve as factors for the severe acute respiratory syndrome coronavirus 2 transmission. So, the purpose of the study was to reveal the impact of air pollutants on COVID-19 severity in Kharkiv. Materials and methods. The influence of environmental factors on the manifestations of COVID-19 epidemic was assessed for 425 days. 16,723 cases of hospitalizations, 1,883 deaths, and 15,146 confirmed cases of COVID-19 in various age groups were studied. There were on average 4,663 active cases every day. Statistical analysis on assessing the impact of environmental factors on COVID-19 morbidity with different degrees of severity was performed using the non-parametric Kruskal-Wallis test in the IBM SPSS Statistics software package, and the normality of the distribution was checked using the Kolmogorov-Smirnov test. Microsoft Office Excel 2016 spreadsheet tools were used for some auxiliary calculations and graphing. Results. The influence of sulfur dioxide, nitrogen dioxide, nitrous oxide, hydrogen sulfide, phenol, soot, and formaldehyde on COVID-19 morbidity in different severity of infection course with incubation periods of 3–4, 6–7, and 10–14 days was revealed. The effect of high concentrations of nitrogen dioxide, nitrogen oxide, and formaldehyde was most significant on active, confirmed, hospitalized, and fatal cases of COVID-19. At the same time, a rise in active cases of COVID-19 was observed with increasing concentrations of sulfur dioxide and hydrogen sulfide, and high concentrations of phenol and soot had an impact on severe forms. Conclusions. Air pollution can contribute to the spread of COVID-19 and lead to its severe forms, which should be considered when predicting morbidity at different levels (national, regional, local) of epidemiological surveillance. Further research is needed to reveal causal relationships between the incidence of COVID-19 and air pollutants, considering the influence of social and natural factors.

Estimating COVID-19 cases on a university campus based on Wastewater Surveillance using machine learning regression models

Wastewater Surveillance (WS) is a crucial tool in the management of COVID-19 pandemic. The surveillance is based on enumerating SARS-CoV-2 RNA concentrations in the community's sewage. In this study, we used WS data to develop a regression model for estimating the number of active COVID-19 cases on a university campus. Eight univariate and multivariate regression model types i.e. Linear Regression (LM), Polynomial Regression (PR), Generalised Additive Model (GAM), Locally Estimated Scatterplot Smoothing Regression (LOESS), K Nearest Neighbours Regression (KNN), Support Vector Regression (SVR), Artificial Neural Networks (ANN) and Random Forest (RF) were developed and compared. We found that the multivariate RF regression model, was the most appropriate for predicting the prevalence of COVID-19 infections at both a campus level and hostel-level. We also found that smoothing the normalised SARS-CoV-2 data and employing multivariate modelling, using student population as a second independent variable, significantly improved the performance of the models. The final RF campus level model showed good accuracy when tested using previously unseen data; correlation coefficient of 0.97 and a mean absolute error (MAE) of 20 %. In summary, our non-intrusive approach has the ability to complement projections based on clinical tests, facilitating timely follow-up and response.

Comparison of kits for SARS-CoV-2 extraction in liquid and passive samples.

Effective extraction and detection of viral nucleic acids from sewage are fundamental components of a successful SARS-CoV-2 sewage surveillance programme. As there is no standard method employed in sewage surveillance, understanding the performance of different extraction kits in the recovery of SARS-CoV-2 and the impact that PCR inhibitors have on quantification is essential to minimize data discrepancies caused by sample extraction. Three commercial nucleic acid extraction kits: the RNeasy PowerSoil Total RNA Kit (PS), the RNeasy PowerMicrobiome Kit (PMB), and the MagMAX™ Microbiome Ultra Nucleic Acid Isolation Kit (MM), with minor modifications, were evaluated. Their efficacy in recovering viral ribonucleic acid and removal of PCR inhibitors was assessed using two South Australian wastewater matrices-one from a major metropolitan site and one from a regional centre. Both had SARS-CoV-2 present due to active COVID-19 cases in these communities. Overall, the MM kit had a higher recovery of SARS-CoV-2 from the samples tested, followed by PMB and PS. The PMB kit performance was strongly influenced by the sample matrix when compared to the MM kit. It is recommended to assess the performance of extraction kits using different local wastewater matrices to ensure the accuracy and reliability of monitoring results to avoid false reporting.

Environmental Factors Influencing the Dynamics and Evolution of COVID-19: A Systematic Review on the Study of Short-Term Ozone Exposure.

The potential influence of environmental factors, particularly air pollutants such as ozone (O3), on the dynamics and progression of COVID-19 remains a significant concern. This study aimed to systematically review and analyze the current body of literature to assess the impact of short-term ozone exposure on COVID-19 transmission dynamics and disease evolution. A rigorous systematic review was conducted in March 2023, covering studies from January 2020 to January 2023 found in PubMed, Web of Science, and Scopus. We followed the PRISMA guidelines and PROSPERO criteria, focusing exclusively on the effects of short-term ozone exposure on COVID-19. The literature search was restricted to English-language journal articles, with the inclusion and exclusion criteria strictly adhered to. Out of 4674 identified studies, 18 fulfilled the inclusion criteria, conducted across eight countries. The findings showed a varied association between short-term ozone exposure and COVID-19 incidence, severity, and mortality. Some studies reported a higher association between ozone exposure and incidence in institutional settings (OR: 1.06, 95% CI: 1.00-1.13) compared to the general population (OR: 1.00, 95% CI: 0.98-1.03). The present research identified a positive association between ozone exposure and both total and active COVID-19 cases as well as related deaths (coefficient for cases: 0.214; for recoveries: 0.216; for active cases: 0.467; for deaths: 0.215). Other studies also found positive associations between ozone levels and COVID-19 cases and deaths, while fewer reports identified a negative association between ozone exposure and COVID-19 incidence (coefficient: -0.187) and mortality (coefficient: -0.215). Conversely, some studies found no significant association between ozone exposure and COVID-19, suggesting a complex and potentially region-specific relationship. The relationship between short-term ozone exposure and COVID-19 dynamics is complex and multifaceted, indicating both positive and negative associations. These variations are possibly due to demographic and regional factors. Further research is necessary to bridge current knowledge gaps, especially considering the potential influence of short-term O3 exposure on COVID-19 outcomes and the broader implications on public health policy and preventive strategies during pandemics.

Statistical explanation of the protective effect of four COVID-19 vaccine doses in the general population.

To assess the effectiveness of four doses of the vaccine against SARS-CoV-2 in the general population and the impact of this on the severity of the disease by age group. By using data from the health authority public data base, we build statistical models using R and the GAMLSS library to explain the behavior of new SARS-CoV-2 infections, active COVID-19 cases, ICU bed requirement total and by age group, and deaths at the national level. The four doses of vaccine and at least the interaction between the first and second doses were important explanatory factors for the protective effect against COVID-19. The R2 for new cases per day was 0.5644 and for occupied ICU beds the R2 is 0.9487. For occupied ICU beds for >70 years R2 is 0.9195 and with the interaction between 4 doses as the main factor. Although the increase in the number of vaccine doses did not adequately explain the decrease in the number of COVID-19 cases, it explained the decrease in ICU admissions and deaths nationwide and by age group.

Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics.

COVID-19 pandemic has questioned the way healthcare operations take place globally as the healthcare professionals face an unprecedented task of controlling and treating the COVID-19 infected patients with a highly straining and draining facility due to the erratic admissions of infected patients. However, COVID-19 is considered as a white swan event. Yet, the impact of the COVID-19 pandemic on healthcare operations is highly uncertain and disruptive making it as a black swan event. Therefore, the study explores the impact of the COVID-19 outbreak on healthcare operations and develops machine learning-based forecasting models using time series data to foresee the progression of COVID-19 and further using predictive analytics to better manage healthcare operations. The prediction error of the proposed model is found to be 0.039 for new cases and 0.006 for active COVID-19 cases with respect to mean absolute percentage error. The proposed simulated model further could generate predictive analytics and yielded future recovery rate, resource management ratios, and average cycle time of a patient tested COVID-19 positive. Further, the study will help healthcare professionals to devise better resilience and decision-making for managing uncertainty and disruption in healthcare operations.

Univariate and Multivariate Long Short Term Memory (LSTM) Model to Predict Covid-19 Cases in Malaysia Using Integrated Meteorological Data

The rate of transmission of coronavirus disease (COVID-19) has been very fast since the first reported case in December 2019 in Wuhan, China. The disease has infected more than 3 million people worldwide and resulted in more than 224 thousand deaths as of May 1, 2020, reported by The World Health Organization (WHO). In the past, meteorological parameters such as temperature and humidity were essential and effective factors against serious infectious diseases such as influenza and Severe Acute Respiratory Syndrome (SARS). Therefore, exploring the relationship between meteorological factors and active COVID-19 cases is essential. This study employs the long-short term memory (LSTM) method to predict Covid-19 Cases in Malaysia. We propose a univariate and multivariate model using Covid-19 cases and meteorology data. The univariate LSTM model uses Covid-19 active cases data in a year as a control attribute for model development. The multivariate LSTM model uses the integrated Covid-19 cases, and meteorology data consists of attributes: minimum, maximum, and average values of Humidity, Temperature, Windspeed, and Pressure from 13 states of Malaysia. The model's performance is evaluated using errors such as MAE, RMSE, MAPE, and the R2 Score. The low errors and higher R2 score indicate the model's excellent performance. We observed that the univariate LSTM model gives the least error in five states, indicating that those states' daily active cases are the main contributing factors. In the multivariate LSTM model, the daily cases and humidity, temperature, and windspeed are the main factors in several different states. The result of the study is to help the government to prevent and manage the spread of the COVID-19 and other upcoming pandemic better.

EARLY-AGE MARRIAGE DURING THE COVID-19 PANDEMIC IN INDONESIA: 2020-2021 KATADATA ANALYSIS

Early marriage is marriage at the age of less than 18 years. Child marriage in Indonesia is still high, and maternal and infant deaths are caused by pregnancy and childbirth. There is no picture of early marriage during the COVID-19 pandemic in Indonesia. This study aims to describe the incidence of early marriage during the COVID-19 pandemic. This type of research uses a narrative approach with a literature review using articles related to early marriage and secondary data from infographic data. Results: Indonesia is one of the countries with the most active cases of COVID-19. The highest number of married girls is in South Kalimantan Province. The dispensation for child marriage in 2020 has tripled from 2019 and is still high in 2021. Indonesian children who marry for the first time are under 15 years old. Girls marry earlier than boys, followed by 1 million teenage pregnancies and four million births in Indonesia. Conclusion: Indonesia is one of the countries with the most active COVID-19 cases in the world, and there has been an increase in cases of early marriage due to applications for child marriages from the lower class, as well as an increase in pregnancies and childbirth.

Gambaran Sikap Ibu Terhadap Pemberian Asi Eksklusif Pada Pasca Pandemi Covid-19 Di Puskesmas Kassi-Kassi Kota Makassar Tahun 2023

Exclusive breastfeeding begins within one hour after the baby is born until the baby is 6 months old. Early Initiation of Breastfeeding (IMD) can help children survive and have the antibodies the body needs to avoid various common diseases, such as pneumonia and diarrhea. Central Statistics Agency (BPS, 2021) data from 2019 to 2021 states that the percentage of babies aged less than 6 months who receive exclusive breastfeeding according to South Sulawesi Province continues to increase every year, namely in 2019 it was 70.52%, in 2020 it was 76.21% and in 2021 it was 76.43%. Analysis of Indonesian Covid-19 data updated as of November 18 2022. Currently there are 6,596,812 confirmed patients, 6,376,205 patients have recovered and 158,323 people have died. The current active cases of Covid-19 in Indonesia are 61,284 people with 6,207 suspects having been examined (Sebaran, 2022). To get an idea of a mother's attitude towards giving exclusive breastfeeding to babies during the post-Covid-19 pandemic at the Kassi-Kassi Health Center, Makassar City in 2022. The method used in this research is a quantitative method with a descriptive approach. The results of this study indicate that the description of mothers' attitudes towards exclusive breastfeeding during the Covid-19 pandemic at the Kassi-Kassi health center in Makassar City is good by looking at the results that have been obtained, namely from 40 respondents who had a good attitude, namely 35 respondents (87, 5%) and 5 respondents (12.5%) had unfavorable attitudes