The Impact of COVID-19 on the Police Personnel Working in Cyberabad

The Impact of COVID-19 on the Police Personnel Working in Cyberabad BIPP Policy Paper 07 Kranthi Kumar Gadidesi The Bharti Institute of Public Policy at the Indian School of Business is a research centre dedicated to promoting evidence-based policymaking in India. As part of its efforts, the centre has launched a Policy Paper Series that provides a platform for researchers, academics, and policymakers to share their insights and research findings on various policy issues. The Policy Paper Series features papers on a range of topics related to public policy, including education, health, environment, governance, and economics. The papers are based on rigorous research and analysis, and they provide insights and recommendations that can inform policy decisions. The Policy Paper Series is a valuable resource for policymakers, researchers, and other stakeholders interested in evidence-based policymaking in India. It provides a forum for researchers and policymakers to engage in a dialogue and exchange ideas and experiences. The papers are also available to the general public, making them an important source of information and knowledge for anyone interested in public policy issues. SERIES EDITOR Anjal Prakash Ashwini Chhatre Aarushi Jain MANAGING EDITOR Nimisha Jain CHIEF COPYEDITOR Smriti Sharma EDITORIAL SUPPORT Anushka Sharma Souparna Biswas GRAPHIC DESIGNER Anil Kumar Singhal In case of any queries, please reach out to [email protected] 01 BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad About BIPP Policy Paper Series Kranthi Kumar Gadidesi December 2025 Bharti Institute of Public Policy (BIPP) Research The Impact of COVID-19 on the Police Personnel Working in Cyberabad 02 BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad Kranthi Kumar Gadidesi is a distinguished Indian Police Service officer of the Jharkhand cadre, 2006 batch. He holds an MBA in Police Management, BSc in Horticulture, and is also an alumnus of the Advanced Management Programme in Public Policy (AMPPP) at ISB. He is currently pursuing a PhD in Public Administration from Sri Venkateswara University, Tirupati. Since joining the IPS in 2006, he has served in some of the most challenging Left-Wing Extremismaffected districts. He held key field postings as SP in Koderma, Latehar, Giridih, and Chaibasa, where he led decisive counterinsurgency operations that significantly reduced Maoist influence. He also served as SP, Special Branch, directing critical Naxal intelligence efforts, and as SP, STF, leading major Naxal operations that ensured peaceful 2019 elections and produced high-impact results. During his central deputation as Director of the Central Detective Training Institute (CDTI), he modernised cybercrime investigation training and secured UTTAM accreditation for CDTI Hyderabad. As IG, Dumka and IG, Bokaro, he effectively countered cybercrime activities, overseeing the arrest of over a thousand cyber-fraud offenders and helping position Jharkhand as a national model. He currently serves as IG, Human Rights, Jharkhand. PUBLISHED BY Bharti Institute of Public Policy, Indian School of Business Mohali Campus: Indian School of Business, Knowledge City, Sector 80, SAS Nagar, Mohali-140306 Hyderabad Campus: Indian School of Business, Gachibowli, Hyderabad-500111 CITATION Gadidesi, K. K. (2025, December). The impact of COVID-19 on the police personnel working in Cyberabad (BIPP Policy Paper Series No. 7). Bharti Institute of Public Policy, Indian School of Business. DOI: https://doi.org/10.5281/zenodo.17863204 03 About the Author BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad Table of Contents List of Tables 5 List of Figures 5 List of Abbreviations 6 7 8 9 2.1 Factors responsible for occupational stress among police personnel 9 2.2 Effects of stress on police personnel 11 2.3 Relation between stress and social factors such as age and gender 11 13 13 14 5.1 Methodology overview: sampling, question types & data collection 14 5.2 Statistical methods 14 15 16 7.1 Descriptive statistical findings on Cyberabad Police 16 personnel who contracted COVID-19 7.2 Hypothesis testing for infection risk, disease severity, 18 and recovery time 7.3 T-test findings: impact of comorbidities and booster 23 on infection risk, severity, and recovery 25 8.1 Result on infection risk, severity, recovery by age, 25 gender, type of duty, diet, exercise 8.2 Result for co-morbidities & vaccination 26 8.3 Discussion 26 28 9.1 Implications 28 9.2 Limitations 30 31 References 33 Appendix A 38 Appendix B 40 Abstract 1. Introduction 2. Literature review 3. Research gap and significance of the study 4. Research questions 5. Research methodology 6. Hypothesis 7. Research findings 8. Result & discussion 9. Implications and limitations 10. Recommendations 04 BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad Table 1: Statistical analysis of the COVID-19 study on police personnel in Hyderabad Table 2: Summary of infection based on type of duty Table 3: Analysis of variance of infection Table 4: Summary of infection based on age, gender, diet and exercise Table 5: Degree of severity based on type of duty Table 6: Analysis of variance of severity Table 7: Two-sample t-test with equal variances for severity Table 8: Summary of recovery based on type of duty Table 9: Analysis of variance of recovery Table 10: Two-sample t-test with equal variances for recovery Table 11: Infectedtwo-sample t-test with equal variances Table 12: Severitytwo-sample t-test with equal variances Table 13: Recoverytwo-sample t-test with equal variances 05 List of Tables Figure 1: Age distribution (Male & Female) Figure 2: Distribution of the type of duties List of Figures BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad ANOVA Analysis of Variance chi2 Chi-squared test COVID-19 Coronavirus Disease 2019 dev. Deviation Df Degrees of Freedom dif. Difference F Frequency H0 Null Hypothesis H1 Alternative Hypothesis MS Mean Square N Number of observations Obs Observation PPE Personal Protective Equipment prob Probability PTSD Post-Traumatic Stress Disorder p-value Probability value (used in hypothesis testing) SARS-CoV-2 Severe Acute Respiratory Syndrome Coronavirus 2 SD / Std.dev. Standard Deviation SPSS Statistical Package for the Social Sciences SS Sum of Squares St Err Statistical Error t-value Test Value 06 List of Abbreviations BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad 07 Frontline workers were one of the most impacted sections of the society during the COVID-19 pandemic. Police duties, which were already stressful, became even more demanding and intense during the pandemic. This study attempts to understand the impact of the pandemic on police personnel by relating it to existing studies on police personnel in different countries. The primary objective of the study was to analyse the various factors responsible for the adverse outcomes due to COVID-19 on police personnel. The aim of the study is to ensure that in the future, police personnel are better equipped to cope with such catastrophes. For the purpose of research, primary data was collected from 1615 police officers on variables such as infections, vaccination, co-morbidities, dietary habits, sleeping habits, stress levels, hospitalisation and recovery. Data has also been collected regarding persistent symptoms following the COVID-19 infection and how this has impacted the lives of these frontline workers. Despite limitations such as the use of retrospective data and a limited sample size, this study’s significance lies in its contribution to the limited literature on the occupational health challenges of police personnel during pandemics in the Indian context. Policymakers and administrators can utilise these findings to design policies, frameworks and strategies that can equip police forces to handle public health emergencies efficiently. The findings reveal that there is a high recovery rate in the police personnel due to the relatively better health status of the police personnel. The study highlights the need for longitudinal surveillance of frontline workers and for pandemic preparedness plans that balance public health mandates with occupational safety. The study moves toward integrated health security frameworks wherein police wellbeing is treated as a cornerstone of community resilience. Such forward looking strategies will be indispensable as cities worldwide confront cascading crises from new disease outbreaks to climate related disasters in the decades ahead. Keywords: COVID-19, police personnel, frontline workers, health impact, public health policy, Cyberabad. Abstract BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad 08 The first COVID-19 case in India was reported on 30 January 2020, with an unexpected peak in April 2021, which profoundly impacted all sections of Indian society. However, the impact of the pandemic was not the same for all sections of society. The frontline workers or the first responders, which involves all essential services such as health workers, paramedics, fire personnel and more importantly, police personnel, were the most affected by the pandemic. Police work involves a unique set of challenges, different from other professions. These challenges eventually bring greater risks, adversely affecting the person physically and psychologically. The inherent stressful nature of police work is described by Singh (2020) as “unlimited and unpredictable”, having “insufficient sleep hours and irregular meals” (Boovaragasamy et al., 2021). COVID-19 only intensified the existing stress and strain among police personnel. The pandemic forced the police in India to deal with unconventional duties and unexpected situations. Navin et al. (2020) list out such duties–implementing lockdowns, restricting public movement, ensuring physical distancing, monitoring COVID-19 hotspots, creating awareness, clarifying fake news, assisting the health department in contact tracing, helping migrant workers and others in need. There are numerous studies which have been conducted to assess the impact of COVID-19 on police personnel, including research on European (Borovec et al., 2022; Frenkel et al., 2021) and Canadian Police forces (Mehdizadeh & Kamkar, 2020). There have also been studies in India, which have a limited focus on South India, especially Telangana. This study focuses on Cyberabad Police personnel, exploring factors, both individual and organisational, linked to infection and adverse outcomes. Data were collected on infection, severity, recovery, lifestyle, and eating habits. The study explores how age, gender, duty type, diet, exercise, and co-morbidities relate to infection and recovery. It also assesses the pandemic’s effect on hospitalisation rates, recovery patterns, occupational stress with respect to sleep patterns, and the effectiveness of vaccination in preventing infection. With these insights, the study aims to better equip police personnel to handle unforeseen public policy crises. 1. Introduction BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad 15 A series of hypotheses was formulated to examine variations in infection risk, severity, and recovery outcomes across demographic, occupational, lifestyle, and clinical factors. Specifically, the hypotheses assessed differences by age, gender, type of duty, dietary preferences, and exercise status. Additional hypotheses were framed to evaluate the influence of comorbidities and vaccination booster doses on infection, severity, and recovery. Each set of hypotheses tested the null assumption of no significant difference between groups against the alternative that differences exist. The complete list of hypotheses is provided in Appendix B. 6. Hypothesis Bartlett’s chi-square tests were conducted. Additionally, t-tests were used to compare mean differences in severity between groups based on comorbidities and vaccination booster dose. For detailed formulae and calculations, refer to BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad 16 7.1 Descriptive statistical findings on Cyberabad police personnel who contracted COVID-19 7. Research findings Table 1: Statistical analysis of COVID-19 study on police personnel in Hyderabad Note. Created by the author using primary data collected through questionnaires. The descriptive statistics seen in Table 1 show the mean, median, standard deviation and number of observations (N) for key numeric indicators related to the study. In Figure 1, there are three general peaks observed in the data regarding age. Most personnel are between the ages of 25-40. Comparing the age distribution by gender in Figure 1, it can be observed that the male population has a far greater proportion of personnel within the age of 40, whereas it is restricted to 2% within the female population. Gender Stats Age Weight (before COVID -19) Weight (after COVID -19) Infected Hospitali sed Severe (Oxygen/Ventilator Needed) Recovered Female Mean 27.45622 55.64186 54.94419 58.99% 9.68% 8.76% 98.44% Median 27 55 53 Standard Deviation 4.60365 9 9.191479 9.717825 49.30% 29.63% 28.33% 12.45% N 217 215 215 217 217 217 128 Male Mean 35.6402 75.18543 76.52916 61.66% 14.31% 6.01% 96.75% Median 34 75 74 Standard Deviation 8.492871 9.851802 40.64142 48.64% 35.03% 23.77% 17.74% N 1398 1386 1389 1398 1398 1398 862 Total Mean 34.5405 6 72.5609 73.63591 61.30% 13.68% 6.38% 96.97% Median 33 74 72 Standard Deviation 8.54766 8 11.82149 38.68993 48.72% 34.38% 24.44% 17.15% N 1615 1601 1604 1615 1615 1615 990 BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad 17 Figure 1: Age distribution (Male & Female) Note. Created by the author using the primary data collected through questionnaires. Figure 2: Distribution of the type of duties Note. Created by the author using the primary data collected through questionnaires. In Figure 2, the main category of type of duty is law and order, with the largest proportions being in desk jobs (24%), and patrolling duty (15%). BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad Number of observations Age 18 7.2 Hypothesis testing for infection risk, disease severity, and recovery time Using ANOVA (analysis of variance), we can test a null hypothesis (typically a status quo claim) against an alternative hypothesis. If the null hypothesis is rejected with a statistically significant measure (e.g., p<0.05), it means that the probability of observing such data assuming the null hypothesis is true is less than 5%. 7.2.1 Outcome 1: infection rate based on type of duty, age, gender, diet and exercise For type of duty, we use the F-test for variance among groups since there are more than 2 categories of duty. Table 2: Summary of infection based on type of duty Note. Created by the author using the primary data collected through questionnaires. BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad Summary of the infected Type of Duty Mean Standard Deviation Frequency Armed Reserve 0.563 0.497 359 Crime & Investigation 0.696 0.461 204 Law and Order (Bandobast) 0.612 0.489 134 Law and Order (Desk job) 0.591 0.492 391 Law and Order (Patrolling) 0.603 0.490 234 Law and Order (Reception) 0.657 0.482 35 Traffic 0.655 0.476 35 Total 0.613 0.487 1,615 ob1 obs2 Mean1 Mean2 dif St Err t-value p-value By age 1265 350 0.591 0.695 -0.104 0.03 -3.55 0.001 By gender 217 1398 0.590 0.617 -0.26 0.036 -0.75 0.452 By diet 322 1293 0.600 0.617 -0.017 0.03 -0.55 0.575 By exercise 734 881 0.624 0.604 0.02 0.025 0.85 0.409 19 Table 3: Analysis of variance of infection Note. Created by the author using the primary data collected through questionnaires. Bartlett's equal-variances test: chi2(6) = 1.8214 Prob>chi2 = 0.935 The conclusion from the table above is that prob>chi2 = 0.935. We only reject H0 if p<0.05 Therefore, there is no conclusive evidence that the infection rate is different among different types of groups i.e. the types of duties considered above. Table 4: Summary of infection based on age, gender, diet and exercise Note. Created by the author using the primary data collected through questionnaires. We reject H0 if p-value < 0.05. This is only true for hypothesis 6.1., by age category, where the mean of second group (age > 40) is higher. Therefore, there is a difference of about 0.695-0.591 = 10.4% greater infection chance for someone above 40 years old, at high statistical significance. Other categories do not show any significant statistical difference or impact. BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad Source SS df MS F Prob>F Between groups 3.059864 6 0.509977365 2.16 0.0445 Within groups 380.067071 1608 0.236360119 Total 383.126935 1614 0.237377283 Source SS df MS F Prob>F Between groups 0.130480846 6 0.021746808 0.36 0.9024 Within groups 96.3004789 1608 0.059888358 Total 96.4309598 1614 0.059746567 20 7.2.2 Outcome 2: severity based on type of duty, age, gender, diet and exercise Table 5: Degree of severity based on type of duty Note. Created by the author using the primary data collected through questionnaires. Table 6: Analysis of variance of severity Note. Created by the author using the primary data collected through questionnaires. Bartlett's equal-variances test: chi2(6) = 14.5509 Prob>chi2 = 0.024 Here, prob>chi2 = 0.024, which is less than 0.05 Hence, from the above table there is a statistically significant difference between different types of duty and severe infection chances. BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad Summary of the infected Type of Duty Mean Standard Deviation Frequency Armed Reserve 0.056 0.230 359 Crime & Investigation 0.049 0.216 204 Law and Order (Bandobast) 0.075 0.264 134 Law and Order (Desk job) 0.064 0.245 391 Law and Order (Patrolling) 0.068 0.253 234 Law and Order (Reception) 0.086 0.284 35 Traffic 0.074 0.262 258 Total 0.064 0.244 1,615 21 Table 7: Two-sample t-test with equal variances for severity Note. Created by the author using the primary data collected through questionnaires. We have p<0.05 only for the age group. Therefore, there is a significantly higher chance of severe infection in individuals above the age of 40 (around 0.095-0.056 = 4% greater chance). Other categories do not show any significant statistical impact or difference. 7.2.3 Outcome 3: recovery based on type of duty, age, gender, diet and exercise Table 8: Summary of recovery based on type of duty Note. Created by the author using data from Cyberabad Police Commissionerate, 2022. BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad ob1 obs2 Mean1 Mean2 dif St Err t-value p-value By age 1265 350 0.056 0.095 -0.039 0.015 -2.65 0.009 By gender 217 1398 0.088 0.06 0.028 0.018 1.55 0.123 By diet 322 1293 0.074 0.061 0.013 0.015 0.9 0.378 By exercise 734 881 0.070 0.059 0.011 0.012 0.85 0.392 Summary of the infected Type of Duty Mean Standard Deviation Frequency Armed Reserve 0.391 0.489 202 Crime & Investigation 0.380 0.487 142 Law and Order (Bandobast) 0.305 0.463 82 Law and Order (Desk job) 0.390 0.489 231 Law and Order (Patrolling) 0.369 0.484 141 Law and Order (Reception) 0.261 0.449 23 Traffic 0.432 0.497 169 Total 0.383 0.486 990 22 Table 9: Analysis of variance of recovery Note. Created by the author using the primary data collected through questionnaires. Bartlett's equal-variances test: chi2(6) = 0.8220 Prob>chi2 = 0.991 Since prob>chi2 = 0.991, we fail to reject H0 that different types of duty have no difference in chances of a quick recovery. Table 10: Two-sample t-test with equal variances for recovery Note. Created by the author using the primary data collected through questionnaires. As seen in the previous two outcomes, again, there is only a difference by age. The chance of a quick recovery drops by 0.415-0.284 = 13% if an individual is above 40 years old. The rest of the groups have no statistical impact on recovery. BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad Source SS df MS F Prob>F Between groups 1.301284 6 0.216880783 0.92 0.4821 Within groups 232.606796 983 0.236629498 Total 233.908080 989 0.236509687 ob1 obs2 Mean1 Mean2 dif St Err t-value p-value By age 747 243 0.415 0.284 0.131 0.036 3.65 0.001 By gender 128 862 0.367 0.385 -0.018 0.046 -0.4 0.697 By diet 193 797 0.394 0.38 0.013 0.039 0.35 0.728 By exercise 458 532 0.380 0.386 -0.005 0.031 -0.15 0.861 ob1 obs2 Mean1 Mean2 dif St Err t-value p-value Infected by 918 697 0.568 0.673 -0.105 0.025 -4.35 0 comorbidities Infected by 440 1175 0.603 0.617 -0.015 0.027 -0.55 0.588 booster dose 23 Table 11: Infectedtwo-sample t-test with equal variances Note. Created by the author using the primary data collected through questionnaires. We observe a difference of -0.105, at <0.1% level of significance (p~0), i.e. around 10% lesser infection rate for those without comorbidities. There is no significant difference of a booster dose in infection rate (p= 0.588). The severity rate is around 3.6% lower (significant at <0.1%, p = 0.003) for individuals without comorbidities. There is no significant difference between individuals with a booster dose and those without. (p = 0.175) 7.3 T-test findings: impact of comorbidities and booster on infection, severity, and recovery Test for Significance in the difference of means for Co-morbidities and Vaccination by booster dose. Table 12: Severitytwo-sample t-test with equal variances Note. Created by the author using the primary data collected through questionnaires. BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad ob1 obs2 Mean1 Mean2 dif St Err t-value p-value Severity by 918 697 0.048 0.85 -0.036 0.012 -3 0.003 comorbidities Severity by 440 1175 0.077 0.059 0.018 0.013 1.35 0.175 booster dose 24 Table 13: Recoverytwo-sample t-test with equal variances Note. Adapted from Cyberabad Police Commissionerate, 2022. From the above table, we fail to reject H0 in both cases – comorbidities and vaccinations (p = 0.399,0.159). There is no significant difference in the ‘quicker recovery’ of individuals who have comorbidities or have received the booster dose. BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad ob1 obs2 Mean1 Mean2 dif St Err t-value p-value Quick recovery 521 469 0.370 0.397 -0.26 0.031 -0.85 0.399 by comorbidities Quick recovery 265 725 0.419 0.369 0.049 0.035 1.4 0.159 by booster dose 31 The study has taken a sample from the Cyberabad Police division of Telangana to ascertain the impact of the COVID-19 pandemic on police personnel. A significant takeaway from the study is the findings related to age. Those who are elderly were found to be more susceptible to infection, experienced high severity of infection and achieved late recovery. Though the study was unable to find out any phenomenal differences in other categories, the analysis still calls for careful observation and policy needs to be formulated accordingly. The present study’s findings agreed with earlier studies worldwide. These findings call for immediate policy reforms and structural changes to ensure the excellent health, vigour and morale of the police personnel. Otherwise, it would be very difficult to handle future pandemics as police personnel constitute one of the most critical frontline essential services in the country. Assessing the outcomes of the study, the following policy prescriptions could be recommended. Although there is only a slight variation with respect to gender, where men were more infected than women, it is still advisable to formulate policies that would take the gender differences into consideration. Secondly, the study found that those who are elderly suffered much from the pandemic and hence the age factor must be the most important element that should go into the future preventive and curative measures against the pandemic. The police personnel studied have shown good recovery rates from the COVID19 disease. There was no difference in infection and recovery rates due to the food habits of the people or the exercises they did. However, as suggested by earlier studies, it is always better to look for improved and advanced coping mechanisms so that the police can fight the disease effectively and recover quickly. Therefore, the study hopes that its findings will be helpful in determining the future course of policymaking concerning pandemic management, especially related to police personnel. 10. Recommendations BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad 32 Drawing from international and national best practices, the Cyberabad Commissionerate should establish a dedicated Occupational Health & Resilience Unit (OHRU) to institutionalise preventive and responsive health measures. This unit can coordinate annual medical and fitness screenings, mental-health assessments, structured fitness and yoga programmes, and nutrition counselling, with ageand comorbidity-based duty allocation during publichealth crises. Canada’s RCMP Well-being Strategy (2021–2024) offers a model of psychological health screenings, peer-support networks, reintegration programmes, and leadership training that ensure early intervention and structured recovery (Royal Canadian Mounted Police, 2024). Similarly, Tamil Nadu’s ‘Magizhchi’ Police Wellness Programme (“DGP launches mental wellbeing”, 2024) shows how state-level initiatives can reduce stress and stigma by offering confidential counselling, family outreach, and holistic practices like yoga and mindfulness. Together, these examples highlight the value of embedding both physical and mental health supports into police culture. The UK Police Wellbeing Framework 2024–2026 (Oscar Kilo, 2024) adds another layer by adopting a “career-stage” model (Join, Train, Work, Live, Leave), rolespecific toolkits, and a self-assessment system, Blue Light Wellbeing Framework (Oscar Kilo, n.d.), that allows forces to benchmark policies, identify gaps, and ensure occupational health standards. By combining role-specific toolkits, occupational health standards, and measurable outcomes, it ensures that wellbeing is embedded across the policing lifecycle. By adapting these elements, Cyberabad can move beyond ad-hoc measures to a structured, lifecycle-based wellness model. Complementary actions should include PPE and vaccine stockpiles, on-site vaccination drives, updated Standard Operating Procedures for infection-control duties, family-inclusive outreach, and a digital health dashboard linked to key performance indicators (e.g., health check coverage, vaccination rates, sick-leave days). With a ring-fenced wellness budget and annual evaluations, such an integrated framework would safeguard personnel well-being, strengthen resilience, and ensure sustained operational readiness during future public-health emergencies. BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad 33 Ab Rahman, F. A., Jody, J. M., Noor, N. H. M., Yusof, M. I. M., & Esa, M. H. (2022). Survey on police stress and conflict during the COVID-19 pandemic. 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The results were compiled, and secondary sources were used to find the similarities and deviations between the findings of the previous studies and the present study. Firstly, it used descriptive statistics, which summarised the basic features of the data, including mean, median and standard deviation. The mean was calculated using the formula, Appendix A Where, x  is the mean, n is the number of observations, and x represents each i individual data point. The standard deviation was measured using: Where, SD is the standard deviation, n is the number of observations, x is each i individual data value, x  is the mean. These helped to assess the distribution of key variables across genders, age groups, hospitalisation, severe and recovered. To explore the relationship between the independent variables (age, gender, type of duty, dietary habits, and exercise), and dependent outcomes (infection, severity, and recovery), a two-way ANOVA (Analysis of Variance) and Bartlett’s chi-square test were used. BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad ANOVA is a statistical test used to analyse the difference between the means of more than two groups. It was measured by: 39 where Y represents the outcome variable, μ is the overall mean, A and B are the ijk i j effects of two independent factors (such as age group and duty type), (AB) is the ij interaction effect, and ϵ is the random error term. Chi-square tests to determine ijk whether the data are significantly different from what was expected, it was measured by: Where, χ² represents the chi-square statistic, Σ means the sum of all categories, O is the observed frequency for a category and E is the expected frequency for i i the same category. A ‘p’ value of less than 0.05 was used to determine the significance of a specific variable. The t-test was implemented to analyse the severity by comorbidities and vaccination booster dose for compare the mean of two group of data. It was measured through, Where, T is t-statistics, var1 and var2 is variance of group 1 and group 2, and n is number of observations. BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad 40 Hypotheses Hypotheses were developed to assess infection risk, severity, and recovery outcomes across demographic (age, gender), occupational (type of duty), lifestyle (dietary habits, exercise), and clinical (comorbidities, vaccination booster dose) factors. The null hypothesis is marked by H0, and the alternative hypothesis is marked by H1. Appendix B 6.1 Infection By age H0 No difference in infection rate Risk between personnel below 40 and above 40. H1 Difference in infection rate between personnel below 40 and above 40. By gender H0 No difference in infection rate between male and female population. H1 Difference in infection rate between male and female population. By type of duty H0 No difference in infection rate between any type of duty. H1 Difference in infection rate between different types of duty. By vegetarian/ H0 No difference in infection rate non-vegetarian between vegetarian and diets non-vegetarian diets. H1 Difference in infection rate between vegetarian and non-vegetarian diets. Section Category Hypothesis Type Statement BIPP POLICY PAPER 07 The Impact of COVID-19 on the Police Personnel Working in Cyberabad