Comparison Analysis of Prediction the Number of Covid-19 Cases Using Support Vector Regression and Long Short-Time Memory

Covid-19 emerged as a pandemic outbreak that spread almost worldwide at the end of December 2019. While this research was carried out, the Covid-19 pandemic was still ongoing. Many countries have made various attempts to overcome Covid-19. In Indonesia, the government and stakeholders, including researchers, have made many activities to reduce the number of positive patients. One of many activities that the government made is the vaccination program. The vaccination program is believed to be the most effective in reducing the number of positive cases of Covid-19. But nobody knows when the Covid-19 pandemic will end. Stakeholder has to know how the trend of Covid-19 cases in Indonesia to make a better decision for facing Covid-19 cases. This study aims to predict the number of positive Covid-19 cases in Indonesia by conducting a comparative analysis performance of Support Vector Regression (SVR) method and Long Short-Term Memory (LSTM) method in machine learning to the prediction of the number of Covid-19 cases. This study was conducted using the dataset Covid-19 in Indonesia from Control Team from 13 January 2021 until 08 November 2021 and with 300 records. The evaluation has been conducted to know the performance of the model prediction number of Covid-19 with Support Vector Regression method and Long Short-Term Memory method based on values of R-Square (R2), the value of Mean Absolute Error (MAE) and Mean Square Error (MSE). The research found that the method Support Vector Regression has better performance than Long Short-Term Memory method for making a prediction of the number Covid-19 using Machine Learning model based on the value of accuracy and error rate based with the value of R-Squared, MAE, and MSE are consecutively 0.902, 0.163, and 0.072. © 2022 IEEE.

Retrospective Assessment of the Accuracy of Mpox Case Projections in New York City

Background: In mid-2022, New York City (NYC) became the epicenter of the US mpox epidemic. Health authorities were in need of forecasts to anticipate the timing and magnitude of the outbreak. We provided mathematical modelbased projections with methodologies that evolved alongside the epidemic. Here, we retrospectively evaluate our mpox case projections and reflect on potential reasons for accuracies and inaccuracies. Method(s): Early in the outbreak (July 1 - 15, 2022), when the size of the at-risk population was unknown, we performed short-term (2-week) forecasting using exponential regression. Once epidemic growth was no longer exponential (July 15 - Aug. 9), we consulted with the NYC Department of Health and Mental Hygiene regarding populations most-at-risk of mpox based on available epidemiological data. Modelers and epidemiologists collaboratively developed an estimate of 70,180 people at risk, informed by estimates of LGBTQ adults with male sex at birth who had 2+ partners in the last 3 months. We combined this with NYC case count data, NYC vaccination data, and global mpox natural history data to develop a Susceptible-Exposed-Infected-Recovered (SEIR) model, taking into account immunity accrued through vaccination and prior exposure, for longer-term forecasting. Result(s): Initial exponential forecasts of the NYC mpox outbreak were only accurate for very short-term predictions (< 2 weeks) (Figure, top panel). Forecasts were more accurate after 1 week (mean absolute error: 83.0 cases/ wk) than after 2 weeks (mean absolute error: 351.4 cases/wk). In contrast, the SEIR model accurately predicted the decline in cases through the end of Sept. 2022, when cases fell to < 70/wk. Over the period from Aug. 10 to Sept. 24 2022, the mean absolute error of the SEIR-based projection was 8.2 cases per week (Figure, bottom panel). Conclusion(s): Model-based NYC mpox projections provided only short-term accuracy in the early epidemic, but long-term accuracy once the epidemic exited exponential growth and an SEIR model was developed. Cumulative cases and vaccinations when exiting exponential growth, and the epidemiology of those most-at-risk, provided evidence for the likely size of the most-at-risk population - a crucial input for an accurate SEIR model. The ability of the SEIR model to accurately forecast mpox cases was in part attributable to lack of vaccine or immune escape by mpox, in contrast to more rapidly-evolving viruses such as SARS-CoV-2.

Forecasting the Temporal Evolution of COVID-19

COVID-19 epidemic has resulted in severe chaos across the globe. Complex frameworks can be investigated and studied using mathematical models, which are reliable and efficient. The objective of this research is to scrutinize the progression and prediction of parameters that evaluate the emergence and transmission of COVID-19 in the two most affected nations, i.e., the USA and India. Five models including the standard and hybrid epidemic models, viz, SIR (Susceptible-Infectious-Removed), SIRD (Susceptible-Infectious-Recovered-Death), SIRD with vaccination, SIRD with vital dynamics (i.e., including birth rate and death rate) and, SIRD with vital dynamics and vaccination have been developed. Worldwide statistics have been observed utilizing graphical layouts. Model evaluation measures such as Mean Absolute error (MAE), Mean-square error (MSE), and Root Mean Square Error (RMSE) for different parameters namely infection rate, recovery rate, and death rate have been estimated. © 2022 IEEE.

Machine Learning Based Predictive Analytics for Agriculture Inventory Management System

When the globe was hit by the vicious Covid 19 pandemic, multiple industries faced the virus's wrath and that included the agricultural warehouse industry. Consequently, many warehouses which had received large shipment stocks of agricultural products were never to be used again as it had reached its expiration date. This led to major losses for the agricultural warehouses as well as losses in crops for farmers and large scale agriculturists. The main objective of this paper is to build a model which utilises 3 heavy-weight algorithms (Seasonal Autoregressive Integrated Moving Average-SARIMA, Long short term memory-LSTM and Holt Winters) and predicts the agricultural needs of retailers and consumers based on previous data from different warehouses. Deploying this system will not help in the regulation of goods in warehouses but will also aid in maximizing the profits and minimizing the losses for warehouses. The algorithm with the least MAE(Mean Absolute Error) value will be considered for forecasting the sales of the aforementioned product. © 2022 IEEE.

Forecasting the Temporal Evolution of COVID-19

COVID-19 epidemic has resulted in severe chaos across the globe. Complex frameworks can be investigated and studied using mathematical models, which are reliable and efficient. The objective of this research is to scrutinize the progression and prediction of parameters that evaluate the emergence and transmission of COVID-19 in the two most affected nations, i.e., the USA and India. Five models including the standard and hybrid epidemic models, viz, SIR (Susceptible-Infectious-Removed), SIRD (Susceptible-Infectious-Recovered-Death), SIRD with vaccination, SIRD with vital dynamics (i.e., including birth rate and death rate) and, SIRD with vital dynamics and vaccination have been developed. Worldwide statistics have been observed utilizing graphical layouts. Model evaluation measures such as Mean Absolute error (MAE), Mean-square error (MSE), and Root Mean Square Error (RMSE) for different parameters namely infection rate, recovery rate, and death rate have been estimated. © 2022 IEEE.

Used Car Price Prediction in Surabaya Using Random Forest Regressor Algorithms

The use of private vehicles during the Covid-19 pandemic has increased because private vehicles, especially cars, are considered as the safest mode of transportation to maintain distance and prevent transmission of the Covid-19 virus. Based on data from two different Indonesian secondary car market place, a comparison of a price sample of Car X in the city of Surabaya with the specifications for the 2015 to 2018 car years with car milage under 1000 kilometers, the used cars have a variety of prices hence a used car price prediction system is needed so that people can find out the average price of used cars sold in the market. In this study the author will use the Random Forest Regressor as a machine learning algorithm to predict the price of a used car with a dataset from the AtapData website. The reason for choosing the Random Forest Regressor is because the algorithm has the power to handle large amounts of data with high dimensions with categorical and numerical data types. The evaluation method used in this study is the Root Mean Absolute Error which produces a value of 0.55612 for validation data and 0.56638 for testing data, while the evaluation proceed with Mean Absolute Error produces a value of 0.45208 for validation data and 0.47576 for testing data. © 2022 IEEE.

Forecasting Omicron Variant of Covid-19 with ANN Model in European Countries – Number of Cases, Deaths, and ICU Patients

Accurate predictions of time series are increasingly required to support judgments in a variety of decisions. Several predictive models are available to support these predictions, depending on how each field offers a data variety with varied behavior. The use of artificial neural networks (ANN) at the beginning of the COVID-19 pandemic was significant since the tool may offer forecasting data for various conditions and hence assist in governing critical choices. In this context, this paper describes a system for predicting the daily number of cases, fatalities, and Intensive Care Unit (ICU) patients for the next 28 days in five European countries: Portugal, the United Kingdom, France, Italy, and Germany. The database selection is based on comparable mitigation processes to analyze the impact of safety procedure flexibilization with the most recent numbers of COVID-19. Additionally, it is intended to check the algorithm's adaptability to different variants throughout time. The network's input data has been normalized to account for the size of the countries in the study and smoothed by seven days. The mean absolute error (MAE) was employed as a comparing criterion of two datasets, one with data from the beginning of the pandemic and another with data from the last year, since all variables (cases, deaths, and ICU patients) may be tendentious in percentage analysis. The best architecture produced a general MAE prediction for the 28 days ahead of 256,53 daily cases, 0,59 daily deaths, and 1,63 ICU patients, all numbers normalized by million people. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Forecasting of Novel Corona Cases in India Using LSTM-Based Recurrent Neural Networks

Novel corona disease is spreading all over the world. The cases of the corona virus are increasing drastically day by day. Therefore, it is necessary to predict the cases in advance to handle the condition. Recently, machine learning comes into the picture of researchers to solve the problem in engineering. The present study is focused to the application of LSTM recurrent neural network to predict the Novel corona cases on the daily basis in India. Various RNN models are used in this study, and performance evaluation of each model is carried out using different statistical parameters such as mean absolute error (MAE), mean absolute percentage error (MAPE), route mean square error (RMSE), and coefficient of determination (r2-score) for regression problems. From the study, it is concluded that the LSTM RNN model can be utilized for the prediction of the novel corona cases. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling.

Controlling the COVID-19 outbreak remains a challenge for Cameroon, as it is for many other countries worldwide. The number of confirmed cases reported by health authorities in Cameroon is based on observational data, which is not nationally representative. The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear. This study aimed to estimate and model the actual trend in the number of COVID -19 new infections in Cameroon from March 05, 2020 to May 31, 2021 based on an observed disaggregated dataset. We used a large disaggregated dataset, and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05, 2020 to May 31, 2021. Subsequently, seasonal autoregressive integrated moving average (SARIMA) modeling was used for forecasting purposes. Based on the prospective MRP modeling findings, a total of about 7450935 (30%) of COVID-19 cases was estimated from March 05, 2020 to May 31, 2021 in Cameroon. Generally, the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times. The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31, 2021. If no action is taken, there could be many waves of the outbreak in the future. To avoid such situations which could be a threat to global health, public health authorities should effectively monitor compliance with preventive measures in the population and implement strategies to increase vaccination coverage in the population.

Regional Prediction of COVID-19 in the United States Based on the Difference Equation Model

The novel coronavirus pneumonia 2019 (COVID-19) has swept the globe in just a few months with negative social and psychological consequences for public health. So far, the United States has been one of the countries most affected by the epidemic. In this study, 51 states in the United States are divided into 10 state clusters according to relevant factors, and a difference equation model with spatio-temporal dynamic characteristics is established to predict the transmission dynamics of COVID-19 in the 10 state clusters and obtain data on regional aggregation levels (the United States). The study showed that the Pearson Correlation Coefficient between the actual data and the predicted data in the 10 state clusters is between 0.6 and 0.96 (mean R2=0.8448), and the mean absolute error (MAE) of the newly confirmed cases in each cluster is between 300 and 1650 (mean MAE=878) and the average forecasting error rate (AFER) of the total confirmed cases in each cluster is between 0.9% and 3% (mean AFER=1.57%). These results show that the difference equation model can well predict the changes in the recent confirmed cases of infectious diseases such as COVID-19. © Pacific Edilite Academic Inc. and Zhejiang Normal University, All rights reserved.

VLSI Implementation of Optimized CNN based COVID-19 Lung Infection Segmentation and Classification from CT Image

The outbreak of COVID-19 challenged the existence of human life on earth. The diagnosis and treatment of this disease is highly crucial in current scenario. Since there is low difference in the intensity of normal cells and affected cells, Computed Tomography (CT) is an efficient tool for the diagnosis of lung infections caused due to COVID-19. In order to train a deep neural network, there is a requirement for huge number of labelled images. There is a necessity to develop an efficient neural network that requires less number of training images. To overcome these problems a novel network is developed for lung CT segmentation (SqueezeNet). For the extraction of energy values from the segmented image, Discrete Wavelet Transform (DWT) with lifting scheme is incorporated in the framework. These energy values are used for training the classifier (ResNet). The entire framework is implemented on hardware using Virtex 2 Pro FPGA. The performance of the proposed system is evaluated using Mean Absolute Error (MAE) and Specificity. The MAE of the system is found to be 0.099, which is very low compared to existing classifiers. The specificity of the system is 0.978, which is higher than that of existing classifiers. © 2022 IEEE.

Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series.

This paper presents a transfer function time series forecast model for COVID-19 deaths using reported COVID-19 case positivity counts as the input series. We have used deaths and case counts data reported by the Center for Disease Control for the USA from July 24 to December 31, 2021. To demonstrate the effectiveness of the proposed transfer function methodology, we have compared some summary results of forecast errors of the fitted transfer function model to those of an adequate autoregressive integrated moving average model and observed that the transfer function model achieved better forecast results than the autoregressive integrated moving average model. Additionally, separate autoregressive integrated moving average models for COVID-19 cases and deaths are also reported.

Swin Transformer for COVID-19 Infection Percentage Estimation from CT-Scans

Coronavirus disease 2019 (COVID-19) is an infectious disease that has spread globally, disrupting the health care system and claiming millions of lives worldwide. Because of the high number of Covid-19 infections, it has been challenging for medical professionals to manage this crisis. Estimating the Covid-19 percentage can help medical staff categorize patients by severity and prioritize accordingly. With this approach, the intensive care unit (ICU) can free up resuscitation beds for the critical cases and provide other treatments for less severe cases to efficiently manage the healthcare system during a crisis. In this paper, we present a transformer-based method to estimate covid-19 infection percentage for monitoring the evolution of the patient state from computed tomography scans (CT-scans). We used a particular Transformer architecture called Swin Transformer as a backbone network to extract the feature from the CT slice and pass it through multi-layer perceptron (MLP) to obtain covid-19 infection percentage. We evaluated our approach on the covid-19 infection percentage estimation challenge dataset, annotated by two expert radiologists. The experimental results show that the proposed method achieves promising performance with a mean absolute error (MAE) of 4.5042, Pearson correlation coefficient (PC) of 0.9490, root mean square error (RMSE) of 8.0964 on the given Val set leaderboard and a MAE of 3.5569, PC of 0.8547 and RMSE of 7.5102 on the given Test set Leaderboard. These promising results demonstrate the high potential of Swin Transformer architecture for this image regression task of covid-19 infection percentage estimation from CT-scans. The source code of this project can be found at: https://github.com/suman560/Covid-19-infection-percentage-estimation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Forecasting the COVID-19 Space-time Dynamics in Brazil with Convolutional Graph Neural Networks and Transport Modals

One of the major challenges imposed by the SARS-CoV-2 pandemic is the lack of pattern in which the virus spreads, making it difficult to create effective policies to prevent and tackle the pandemic. Several approaches have been proposed to understand the virus behavior and anticipate its infection and death curves at country ans state levels, thus supporting containment measures. Those initiatives generalize well for general extents and decisions, but they do not predict so well the trajectory of the virus through specific regions, such as municipalities, considering their distinct interconnection profiles. Specially in countries with continental dimensions, like Brazil, too general decisions imply that containment measures are applied either too soon or too late. This study presents a novel scalable alternative to forecast the numbers of case and death by SARS-CoV-2, according to the influence that certain regions exert on others. By exploiting a single-model architecture of graph convolutional networks with recurrent networks, our approach maps the main access routes to municipalities in Brazil using the modals of transport, and processes this information via neural network algorithms to forecast at the municipal level ans for the whole country. We compared the performance in forecasting the pandemic daily numbers with three baseline models using Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (sMAPE) and Normalized Root Mean Square Error (NRMSE) metrics, with the forecasting horizon varying from 1 to 25 days. Results show that the proposed model overcomes the baselines when considering the MAE and NRMSE (p ˂0.01), being specially suitable for forecasts from 14 to 24 days ahead. Author

Ventilation indices for evaluation of airborne infection risk control performance of air distribution.

Air distribution is an effective engineering measure to fight against respiratory infectious diseases like COVID-19. Ventilation indices are widely used to indicate the airborne infection risk of respiratory infectious diseases due to the practical convenience. This study investigates the relationships between the ventilation indices and airborne infection risk to suggest the proper ventilation indices for the evaluation of airborne infection risk control performance of air distribution. Besides the commonly used ventilation indices of the age of air (AoA), air change effectiveness (ACE), and contaminant removal effectiveness (CRE), this study introduces two ventilation indices, i.e., the air utilization effectiveness (AUE) and contaminant dispersion index (CDI). CFD simulations of a hospital ward and a classroom served by different air distributions, including mixing ventilation, displacement ventilation, stratum ventilation and downward ventilation, are validated to calculate the ventilation indices and airborne infection risk. A three-step correlation analysis based on Spearman's rank correlation coefficient, Pearson correlation coefficient, and goodness of fit and a min-max normalization-based error analysis are developed to qualitatively and quantitatively test the validity of ventilation indices respectively. The results recommend the integrated index of AUE and CDI to indicate the overall airborne infection risk, and CDI to indicate the local airborne infection risk respectively regardless of the effects of air distribution, supply airflow rate, infectivity intensity, room configuration and occupant distribution. This study contributes to airborne transmission control of infectious respiratory diseases with air distribution.

Predicting Covid-19 by Referring Three Supervised ML Algorithms: A Comparative Study using WEKA

Early disease detection plays a crucial role in preventing the spread of a life threatening disease. COVID-19, a contagious disease that has mutated into several strains, has now become a global pandemic by necessitating the need to implement immediate disease diagnosis and detection. As technology advances, the quantity of information available about COVID-19 increases day by day, and further the data mining process can be utilized to extract the important information from huge amounts of data. Multiple supervised ML approaches were utilized to create a model for assessing and predicting the existence of COVID-19 by using the Kaggle dataset. WEKA was used to implement the J48 Decision Tree, Random Forest, and Naive Bayes algorithms. The performance of each and every model is then compared by using ten-fold cross validation with important accuracy measures, such as properly or erroneously categorized examples, Kappa Statistics, Mean Absolute Error (MAE), and time spent in developing the model. The resultant research findings conclude that the Random Forest [RF] algorithm outperforms other methods by providing a mean absolute error of 0.022 and accuracy of about 98.81 %. © 2022 IEEE.

LSTM-based Model for Forecasting of COVID-19 Vaccines in Pakistan

COVID-9 has infected nearly every country on the planet. As a result, vaccinations that can reduce our risk of contracting and spreading the COVID19 virus have been developed. As a result, each government must determine how long it will take to properly vaccinate all of its population. In this study, we built an LSTM-based prediction model to anticipate vaccination coverage in Pakistan and India. The dataset contains records of vaccine updated till January 2022. To measure the losses, we have used mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE) and Root mean squared error (RMSE). The model performs very well on training and testing datasets. This model can help government in the vaccination campaign. © 2022 IEEE.

Forecasting of Individuals' Movement in Society After Corona Pandemic Using RNNs with LSTM and GRUs

The health crisis that attributed to the quick spread of the COVID-19 has impacted the globe negatively in terms of economy, education, and transport and led to the global lockdown. The risk of the COVID-19 infection has been increased due to a lack of a successful cure for the disease. Thus, social distancing is considered the most appropriate precaution measure to control the viral spread throughout the world. In this study, a model was proposed for deep learning capable of predicting the movement of people in the pandemic in the short term (one day) to take precautions and control the COVID-19 infection. The proposed model consists of four phases: data collection, pre-processing phase, prediction stage, and evaluation and Comparison phase. The dataset is obtained from 428 mobility reports, collected based on data from users that have been selected for their Google Account location history for a country such as Iraq for 428 days. A deep learning algorithm such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and hybrid model (GRU&LSTM) is applied to pre-processed data to predict the movement of people. They are compared using statistical measures: Mean absolute error (MAE) and root mean square error (RMSE) for performance measurement of these machine learning algorithms. The results of the GRU are the sum of MAE 0.4277 and sum of RMSE 0.6470 for predict person path and movement with training time equal to 33.189 sec, while the results of the hybrid model are the sum of MAE 0.4355 and sum of RMSE 0.6563 for prediction and the training time equal to 53.144 sec, and the results of the LSTM are the sum of MAE 0.4395 and sum of RMSE 0.6612 for prediction and the training time equal to 100.752 sec. These statistical measurement values indicate proposed model GRU outperformed all other models, it showed a solid performance to predict person path and movement in coronavirus pandemic and took little time to train compared to other algorithms, while the hybrid algorithm showed good performance and a short period in training compared with the LSTM model. © 2021 IEEE.

Forecasting Daily Patient Arrivals during COVID-19 in Emergency Departments: A Deep Learning Approach

Study Objectives: As the fourth wave of coronavirus disease 2019 (COVID-19) surges in Michigan, most health care systems are experiencing an increased hospitalization rate of infected COVID-19 patients. Understanding the arrival rates of patients to the emergency department (ED) is fundamental in managing the limited health care resources. Our objective is to develop an accurate forecasting model based on ED patients’ arrival and COVID-19 status to help manage and facilitate a data-driven resource planning. Methods: A cohort study of patients with clinical suspicion of COVID-19 evaluated at 2 EDs within an integrated health system that cares for a racially diverse population. We included patient arrivals, COVID-19 status, and demographic information between the dates of January 1, 2020 and March 16, 2021. We developed deep learning models (Long Short-Term Memory (LSTM)) to forecast patient arrivals in two geographically diverse EDs (denoted as ED1 and ED2). We used data from January to December 2020 for model training and data from January 2021 to March 2021 for model validation. The models are evaluated based on the root mean squared error (RMSE), the square root of the average of the squared error between predicted and observed values, and the mean absolute error (MAE), which provides the mean absolute difference between the predicted and the observed ED patient arrival rates per day. Results: In ED1, there were 56, 61 total patient arrivals (1, 433 infected COVID-19 patients) with a mean age of 38.0 ± 21.2 years. A majority were female (33, 457, 59.1%) and 29, 040 (51.3%) were Black. The average patient arrival per day was 125.1 (SD 35.0) for those without COVID-19, and 3.3 (SD 3.6) for COVID-1 confirmed patients. In ED2, there were 74, 176 total patient arrivals (1, 546 infected COVID-19 patients) with a mean age of 45.0 ± 23.0 years. A majority were female (39, 521, 53.3%) and 10, 636 (14.3%) were Black. The average patient arrival per day was 164.7 (SD 33.2) for those without COVID-19, and 3.5 (SD 5.0) for COVID 19 confirmed patients. Figure 1 shows the observed and predicted patients’ arrival for the two EDs for regular and confirmed COVID-19 patients. The LSTM models show accurate prediction one week in advance of daily patient arrivals for ED1 and ED2 with RMSE scores of 17 and 20 patients, respectively. The MAE values imply that, on average, the forecast’s error from the true daily patient arrival rate is 13.9 and 16.0 for ED1 and ED2, respectively. For COVID-19 patient arrivals to ED1 and ED2, the RMSE score is 3 patients each, while th MAE values are 2.2 and 2.4, respectively. Conclusion: This study demonstrates that an average RMSE prediction score of 18.5 and 3 patient arrivals per day for regular and COVID-19 confirmed patients is possible across EDs using LSTM one week prior to forecasting. Future validation and implementation of such forecasting models could impact effective planning and allocation of limited ED and hospital resources. [Formula presented]

Predicting New Covid-19 Outbreak Clusters among the "science Denial" Communities

The term "herd stupidity"has recently gone viral as an innuendo for our country's stuttering in dealing with the Covid-19 pandemic. We assessed indications of "science denial"text analysis on social media (Twitter). We developed a science denial indication dataset by utilizing the social network analysis (SNA) tool and taking geolocation data and the active cases data from the Covid-19 National Task Force regarding the distribution of clusters. We applied regression as prediction algorithms to predict areas that could become new clusters of Covid-19 spread. We tested the performance of the prediction algorithm using the mean absolute error (MAE). The experimental results show a correlation between the level of "science denial"and the formation of new clusters. The results of the prediction performance measurement show that the prediction algorithm gives acceptable results with a value of 843 MAE. This study demonstrated that Banten is the province with the highest percentage of negative sentiment and science denial text. The output provides a basis for policymakers to determine appropriate interventions in the context of controlling the Covid-19 pandemic, especially in Indonesia. © 2021 ACM.