Stringency Index Research Articles

Reproductive health crisis during waves one and two of the COVID-19 pandemic in India: Incidence and deaths from severe maternal complications in more than 202,000 hospital births.

BackgroundThe SARS-CoV-2 pandemic in India has adversely affected many aspects of population health. We need detailed evidence of the impact on reproductive health in India so that lessons can be learnt.MethodsHospital-based repeated monthly survey of nine severe maternal complications and death in 15 hospitals across five states in India covering a total of 202,986 hospital births, December-2018 through to May-2021. We calculated incidence rates (with 95% confidence intervals (CIs)) per 1000 hospital births, case-fatality and rate ratios (RR) with 95% CIs. Linear regression was used to examine the association between the Government Response Stringency Index (GRSI) for India and changes in hospital births, incidence and case-fatality.FindingsThere was a significant decrease in hospital births per month during the pandemic period with a 4.8% decrease per 10% increase in the GRSI scores (p < 0.001). The overall incidence of severe complications in the pandemic period was not significantly different from the pre-pandemic period, but hospital admissions from septic abortion was 56% higher (RR=1.56; 95% CI=1.22–1.99; p < 0.001). The overall case-fatality of complications increased by 23% (RR=1.23; 95% CI=1.03–1.46; p = 0.022) and remained high across the different phases of the pandemic with a notable significant increase in deaths from heart failure in pregnancy.InterpretationOur study supports the legitimacy of the calls made to maintain sexual and reproductive health services as essential services during the pandemic. Lessons learnt should be used to avert the ongoing reproductive health crisis while India plans to manage a third wave of the pandemic.FundingThe MaatHRI platform and this study are funded by a Medical Research Council Career Development Award to MN (Ref:MR/P022030/1). The funder has no role in the study design, data collection, analysis, or writing the paper

COVID-19 CASES, MEDIA ATTENTION AND SOCIAL MOOD

The study uses rolling regressions, both at global and country level, to analyze the impact of daily Covid-19 case numbers on four (Panic, Sentiment, Media coverage, and Fake news) indices. The indices are obtained from the Ravenpack Finance, while the daily Covid-19 cases and the policy response stringency index data is extracted from the Oxford COVID-19 Government Response Tracker. The results indicate that the impact of the number of daily Covid-19 cases on the indices is quite variable over time. Higher impact on the indices is reflected in periods where there is a significant surge in cases, in particular the initial surge in Spring, Summer and Fall. There is some evidence of diminished (increased) sensitivity of panic and media indices (fake news) to number of cases but this is not consistent across all countries. These results indicate that the public are concerned and respond to changes in the trends of the spread of the virus and highlight the importance of managing trends if halting its spread is not immediately feasible.Keywords: COVID-19, Ravenpack Indices, Media Attention, Stringency IndexJEL Classifications: G40, G41DOI: https://doi.org/10.32479/ijefi.11591

Institutional and cultural determinants of speed of government responses during COVID-19 pandemic

This article examines institutional and cultural determinants of the speed of government responses during the COVID-19 pandemic. We define the speed as the marginal rate of stringency index change. Based on cross-country data, we find that collectivism is associated with higher speed of government response. We also find a moderating role of trust in government, i.e., the association of individualism-collectivism on speed is stronger in countries with higher levels of trust in government. We do not find significant predictive power of democracy, media freedom and power distance on the speed of government responses.

Government responses and COVID-19 deaths: Global evidence across multiple pandemic waves.

We provide an assessment of the impact of government closure and containment measures on deaths from COVID-19 across sequential waves of the COVID-19 pandemic globally. Daily data was collected on a range of containment and closure policies for 186 countries from January 1, 2020 until March 11th, 2021. These data were combined into an aggregate stringency index (SI) score for each country on each day (range: 0-100). Countries were divided into successive waves via a mathematical algorithm to identify peaks and troughs of disease. Within our period of analysis, 63 countries experienced at least one wave, 40 countries experienced two waves, and 10 countries saw three waves, as defined by our approach. Within each wave, regression was used to assess the relationship between the strength of government stringency and subsequent deaths related to COVID-19 with a number of controls for time and country-specific demographic, health system, and economic characteristics. Across the full period of our analysis and 113 countries, an increase of 10 points on the SI was linked to 6 percentage points (P < 0.001, 95% CI = [5%, 7%]) lower average daily deaths. In the first wave, in countries that ultimately experiences 3 waves of the pandemic to date, ten additional points on the SI resulted in lower average daily deaths by 21 percentage points (P < .001, 95% CI = [8%, 16%]). This effect was sustained in the third wave with reductions in deaths of 28 percentage points (P < .001, 95% CI = [13%, 21%]). Moreover, interaction effects show that government policies were effective in reducing deaths in all waves in all groups of countries. These findings highlight the enduring importance of non-pharmaceutical responses to COVID-19 over time.

COVID-19 in Asia: Transmission factors, re-opening policies, and vaccination simulation

This work aims to provide insights on the COVID-19 pandemic in three prime aspects. First, we attempted to understand the association between the COVID-19 transmission rate, environmental factors (air pollution, weather, mobility), and socio-political parameters (Government Stringency Index, GSI). Second, we evaluated the efficiency of various strategies, including radical opening, intermittent lockdown, phase lift, and contact tracing, to exit the COVID-19 pandemic and get back to pre-pandemic conditions using a stochastic individual-based epidemiology model. Third, we used a deep learning approach and simulated the vaccination rate and the time for reaching herd immunity. The analysis was done based on the collected data from eight countries in Asia, including Iran, Turkey, India, Saudi Arabia, United Arab Emirates, the Philippines, South Korea, and Russia (as a transcontinental country). Our findings in the first part highlighted a noninfluential impact from the weather-driven parameters and short-term exposure to pollutants on the transmission rate; however, long-term exposure could potentially increase the risk of COVID-19 mortality rates (based on 1998–2017 p.m.2.5 data). Mobility was highly correlated with the COVID-19 transmission and based on our causal analysis reducing mobility could curb the COVID-19 transmission rate with a 6-day lag time (on average). Secondly, among all the tested policies for exiting the COVID-19 pandemic, the contact tracing was the most efficient if executed correctly. With a 2-day delay in tracing the virus hosts, a 60% successful host tracing, and a 70% contact reduction with the hosts, a pandemic will end in a year without overburdening a healthcare system with 6000 hospital beds capacity per million. Lastly, our vaccine simulations showed that the target date for achieving herd immunity significantly varied among the countries and could be delayed to October–november 2022 in countries like India and Iran (based on 60% immunized population and assuming no intermediate factors affecting the vaccination rate).

COVID-19 pandemic in BRICS countries and its association with socio-economic and demographic characteristics, health vulnerability, resources, and policy response

BackgroundLittle attention has been paid to the comparison of COVID-19 pandemic responses and related factors in BRICS (Brazil, Russia, India, China, and South Africa) countries. We aimed at evaluating the association of daily new COVID-19 cases with socio-economic and demographic factors, health vulnerability, resources, and policy response in BRICS countries.MethodsWe conducted a cross-sectional study using data on the COVID-19 pandemic and other indicators of BRICS countries from February 26, 2020 to April 30, 2021. We compared COVID-19 epidemic in BRICS countries and analyzed related factors by log-linear Generalized Additive Model (GAM) models.ResultsIn BRICS countries, India had the highest totally of confirmed cases with 18.76 million, followed by Brazil (14.45 million), Russia (4.81 million), and South Africa (1.58 million), while China (0.10 million) had the lowest figure. South Africa had the lowest rate of administered vaccine doses (0.18 million) among BRICS countries as of April 30, 2021. In the GAM model, a 1 unit increase in population density and policy stringency index was associated with a 5.17% and 1.95% growth in daily new COVID-19 cases (P < 0.001), respectively. Exposure–response curves for the effects of policy stringency index on daily new cases showed that there was a rapid surge in number of daily new COVID-19 cases when the index ranged from 0 to 45. The number of infections climbed slowly when the index ranged from 46 to 80, and decreased when the index was above 80 (P < 0.001). In addition, daily new COVID-19 cases (all P < 0.001) were also correlated with life expectancy at birth (-1.61%), extreme poverty (8.95%), human development index (-0.05%), GDP per capita (-0.18%), diabetes prevalence (0.66%), proportion of population aged 60 and above (2.23%), hospital beds per thousand people (-0.08%), proportion of people with access to improved drinking water (-7.40%), prevalence of open defecation (0.69%), and annual tourist/visitor arrivals (0.003%), after controlling other confounders. Different lag structures showed similar results in the sensitivity analysis.ConclusionsStrong policy response is crucial to control the pandemic, such as effective containment and case management. Our findings also highlighted the importance of reducing socio-economic inequalities and strengthening the resilience of health systems to better respond to public health emergencies globally.Graphic abstract

Prevalence and Associated Risk Factor of COVID-19 and Impacts of Meteorological and Social Variables on Its Propagation in Punjab, Pakistan.

The current study identifies the spatial distribution of COVID-19 cases and its association with meteorological and social variables in Punjab (densely populated province of Pakistan). To identify the COVID-19 propagation, the weekly growth, recovery, and deaths rate have also been calculated. The geographic information system (GIS) has used to determine COVID-19 impacts on gender (male/female), age groups, and causalities over an affected population (km−2) for the period of 11th March to 12th August, 2020 in each district of province. Our results show that 43 peak days (where daily positive cases were above 900) have been observed in Punjab during 27th May to 8th July, 2020. The high population density districts, i.e., Lahore and Islamabad, have been affected (five persons per square kilometers) due to COVID-19, whereas the maximum death tolls (> 50 persons per millions) have also been observed in these urban districts. The meteorological variables (temperature, humidity, heat index, and ultraviolet index) show negative significant relationship to basic reproduction number (R0), whereas daily COVID-19 cases are positively correlated to aerosols concentration at 95% confidence level. The government intervention (stringency index) shows a positive impact to reduce the COVID-19 cases over the province. Keeping in view the COVID-19 behavior and climatology of the region, it has been identified that the COVID-19 cases may likely to increase during the dry period (high concentration of aerosols) i.e., October–December, 2020 and post-spring season (April to June), 2021 in urban areas of Pakistan. This study provides an overview on districts vulnerability that would help the policy makers, health agencies to plan their activities to reduce the COVID-19 impacts.

On the Effectiveness of COVID-19 Restrictions and Lockdowns: Pan Metron Ariston

I examine the dynamics of confirmed case (and death) growth rates conditional on different levels of severity in implemented NPIs, the mobility of citizens and other non restrictive policies. To account for the endogeneity of many of these variables, and the possibility of correlated latent (unobservable) country characteristics, I estimate a four structural model of the evolution of case growth rates, death growth rates, average changes in mobility and the determination of the severity of NPIs. There are strongly decreasing returns to the stringency of NPIs, especially for extreme lockdowns, as no significant improvement in the main outcome measures is found beyond NPIs corresponding to a Stringency Index range of 51–60 for cases and 41–50 for deaths. A non-restrictive policy of extensive and open testing has half of the impact on pandemic dynamics as the optimal NPIs, with none of the associated social and economic costs resulting from the latter. Decreases in mobility were found to increase, rather than decrease case growth rates, consistent with arguments that within-household transmission–resulting from spending more time at residences due to mobility restrictions–may outweigh the benefits of reduced community transmission. Vaccinations led to a fall in case and death growth rates, however the effect size must be re-evaluated when more data becomes available. Governments conditioned policy choice on recent pandemic dynamics, and were found to de-escalate the associated stringency of implemented NPIs more cautiously than in their escalation, i.e., policy mixes exhibited significant hysteresis. Finally, at least 90% of the maximum effectiveness of NPIs can be achieved by policies with an average Stringency index of 31–40, without restricting internal movement or imposing stay at home measures, and only recommending (not enforcing) closures on workplaces and schools, accompanied by public informational campaigns. Consequently, the positive effects on case and death growth rates of voluntary behavioral changes in response to beliefs about the severity of the pandemic, generally trumped those arising from mandatory behavioral restrictions. The exception being more stringent mandatory restrictions on gatherings and international movement, which were found to be effective. The findings suggest that further work should be directed at re-evaluating the effectiveness of NPIs, particularly towards empirically determining the optimal policy mix and associated stringency of individual NPIs.

The relationship between time to a high COVID-19 response level and timing of peak daily incidence: an analysis of governments\u2019 Stringency Index from 148 countries

BackgroundThe transmission dynamics and severity of coronavirus disease 2019 (COVID-19) pandemic is different across countries or regions. Differences in governments’ policy responses may explain some of these differences. We aimed to compare worldwide government responses to the spread of COVID-19, to examine the relationship between response level, response timing and the epidemic trajectory.MethodsFree publicly-accessible data collected by the Coronavirus Government Response Tracker (OxCGRT) were used. Nine sub-indicators reflecting government response from 148 countries were collected systematically from January 1 to May 1, 2020. The sub-indicators were scored and were aggregated into a common Stringency Index (SI, a value between 0 and 100) that reflects the overall stringency of the government’s response in a daily basis. Group-based trajectory modelling method was used to identify trajectories of SI. Multivariable linear regression models were used to analyse the association between time to reach a high-level SI and time to the peak number of daily new cases.ResultsOur results identified four trajectories of response in the spread of COVID-19 based on when the response was initiated: before January 13, from January 13 to February 12, from February 12 to March 11, and the last stage—from March 11 (the day WHO declared a pandemic of COVID-19) on going. Governments’ responses were upgraded with further spread of COVID-19 but varied substantially across countries. After the adjustment of SI level, geographical region and initiation stages, each day earlier to a high SI level (SI > 80) from the start of response was associated with 0.44 (standard error: 0.08, P < 0.001, R2 = 0.65) days earlier to the peak number of daily new case. Also, each day earlier to a high SI level from the date of first reported case was associated with 0.65 (standard error: 0.08, P < 0.001, R2 = 0.42) days earlier to the peak number of daily new case.ConclusionsEarly start of a high-level response to COVID-19 is associated with early arrival of the peak number of daily new cases. This may help to reduce the delays in flattening the epidemic curve to the low spread level.Graphic abstract

Covid-19 Pandemic Outbreak: The Asean-5 Stock Market Short-Term And Long-Term Responses

The aim of this paper is to empirically examine the effect of COVID-19 pandemic on the stock market performance in the ASEAN-5 countries. By utilizing the 2020’s daily available data, the panel cointegration results reveal that a long-term cointegrating relationship exists between daily new cases and total death due to COVID-19 on the ASEAN-5’s stock market performance. Also, the Stringency Index has a significant effect on the stock market in the short-run. Thus, prudential initiatives to reduce the number of new cases and death due to COVID-19 are the primary goals to retain the performance of ASEAN-5’s stock market. Besides that, necessary actions taken to limit the business activities should also be supervised to maintain the performance of related industries which subsequently affect the stock market performance across the ASEAN-5 countries. © Rigeo Review Of International Geographical Education

A chemical engineer's take of COVID-19 epidemiology.

SARS‐CoV‐2, a novel coronavirus spreading worldwide, was declared a pandemic by the World Health Organization 3 months after the outbreak. Termed as COVID‐19, airborne or surface transmission occurs as droplets/aerosols and seems to be reduced by social distancing and wearing mask. Demographic and geo‐temporal factors like population density, temperature, healthcare system efficiency index and lockdown stringency index also influence the COVID‐19 epidemiological curve. In the present study, an attempt is made to relate these factors with curve characteristics (mean and variance) using the classical residence time distribution analysis. An analogy is drawn between the continuous stirred tank reactor and infection in a given country. The 435 days dataset for 15 countries, where the first wave of epidemic is almost ending, have been considered in this study. Using method of moments technique, dispersion coefficient has been calculated. Regression analysis has been conducted to relate parameters with the curve characteristics.

Interplay between climate, pollution and COVID-19 on ST-elevation myocardial infarction in a large metropolitan region.

Collective risk factors such as climate and pollution impact on the risk of acute cardiovascular events, including ST-elevation myocardial infarction (STEMI). There is limited data however on the precise temporal and independent association between these factors and STEMI, and the potentially interacting role of government policies against Coronavirus disease 2019 (COVID-19), especially for Latin America. We retrospectively collected aggregate data on daily STEMI admissions at 10 tertiary care centers in the Buenos Aires metropolitan area, Argentina, from January 1, 2017 to November 30, 2020. Daily measurements for temperature, humidity, atmospheric pressure, wind direction, wind speed, and rainfall, as well as carbon monoxide (CO), nitrogen dioxide, and particulate matter <10 µm (PM10), were retrieved. Exploratory analyses focused on key COVID-19-related periods (e.g. first case, first lockdown), and Stringency Index quantifying the intensity of government policy response against COVID-19. A total of 1498 STEMI occurred over 1430 days, for an average of 0.12 STEMI per center (decreasing from 0.130 in 2018 to 0.102 in 2020, P=0.016). Time series analysis showed that lower temperature and higher concentration of CO and PM10 were all significantly associated with an increased rate of STEMI (all P<0.05), whereas COVID-19 outbreak, lockdown, and stringency of government policies were all inversely associated with STEMI (all P<0.05). Notably, environmental features impacted as early as 28 days before the event (all P<0.05), even if same or prior day associations proved stronger (all P<0.05). Multivariable analysis suggested that maximum temperature (P=0.001) and PM10 (P=0.033) were the strongest predictor of STEMI, even after accounting for COVID-19-related countermeasures (P=0.043). Lower temperature and higher concentrations of CO and PM10 are associated with significant increases in the rate of STEMI in a large Latin American metropolitan area. The reduction in STEMI cases seen during the COVID-19 pandemic is at least in part mediated by improvements in pollution, especially reductions in PM10.

Effects of quarantine disobedience and mobility restrictions on COVID-19 pandemic waves in dynamical networks.

For the global COVID-19 pandemic it is still not adequately understood how quarantine disobedience and change in mobility restrictions influence the pandemic spreading and waves. Here, we propose a new metapopulation epidemiological model as a network composed of equal clusters to predict the course of the epidemic based on the contiguous spreading between the neighbours, the probability of quarantine misbehaviour, and the probability of mobility, which control contacts outside the cluster. We exemplify the model by comparing simulation results with real data on COVID-19 pandemic in Croatia. Fitting the data over the first and second pandemic waves, when the probability of mobility is set by the stringency index, the probability of quarantine misbehaviour is found by a Bayesian optimization yielding a fascinating agreement between the daily COVID-19 deaths and model output and efficiently predicting the timing of pandemic bursts. A sudden increase in the probability of quarantine misbehaviour alongside the sudden increase in the probability of mobility generate the model third wave in good agreement with daily COVID-19 deaths.

Paediatric asthma exacerbation admissions and stringency of non-pharmaceutical interventions: Results from a developing country.

Non-pharmaceutical interventions (NPI) mitigation measures implemented to mitigate COVID-19 has brought unprecedented global impact. Changes in daily living routines, reduction in physical activities, and changes in environmental indicators were suggested to positively impact paediatric asthma indicators in western cultures. Little is known about such impact in developing countries. This study investigated the potential impact of COVID-19 NPI measures on paediatric asthma exacerbation admissions in Northern Jordan. Aggregate data from a paediatric hospital, Princess Rahma Pediatric Teaching Hospital, and King Abdulla University Teaching Hospital were utilised from 2018 to 2020. The number of paediatric asthma exacerbation admissions was calculated by week and compared by year, lockdown status, and weeks for lockdown (weeks 13-19). The stringency index was correlated with the number of weekly paediatric asthma exacerbation admissions for 2020. A total of 1,207 paediatric asthma exacerbation admissions were reported; 40.3%, 35.2%, and 24.5% in 2018, 2019 and 2020, respectively. The number of weekly admissions ranged from 1 to 13 paediatric asthma cases (mean (SD) = 7.6 (2.7)). In 2018 and 2019, respectively, mean weekly admissions were 9.2 (1.9) and 8.0 (2.0) paediatric asthma cases, while in 2020 the mean was 5.6 (2.7) cases. Significant differences in mean weekly paediatric asthma exacerbation admissions were detected by year, with mean weekly admissions for 2020 being significantly lower than that for 2018 and 2019. During the lockdown (22 March to 1 May 2020), mean weekly admissions (2.6 (1.4)) was significantly lower than that before the lockdown (8.6 (2.0)) and after the lockdown (5.2 (2.0)). For the lockdown weeks only, the mean admissions for the year 2020 was significantly lower than that for years 2019 and 2018. The stringency index negatively correlated with the number of weekly admissions. NPI measures in Jordan seem to have positively impacted paediatric asthma exacerbation admissions. Further studies are needed to draw public health policies and fine-tune environmental and community policies.

CASES, DEATHS, STRINGENCY INDEXES AND INDIAN FINANCIAL MARKET — EMPIRICAL EVIDENCE DURING COVID-19 PANDEMIC

The primary motive of the present study is to investigate the effect of COVID-19 pandemic on the Indian financial market. In this paper, we explain financial market volatility through stock market indices due to changes in the total number of COVID-19 cases, deaths and stringency index over a study period. The results of the study show that there exists a significant relationship between the total number of confirmed cases, total deceased due to COVID-19, and the country’s response towards pandemic i.e., stringency index and considered stock indices of Indian financial market. For the proposed analysis, we use ordinary least square regression models with residual diagnosis.

Are Climate Change Policies Politically Costly?

Are policies designed to avert climate change (Climate Change Policies, or CCPs) politically costly? Using data on governmental popular support and the OECD’s Environmental Stringency Index, we find that CCPs are not necessarily politically costly: policy design matters. First, only market-based CCPs (such as emission taxes) generate negative effects on popular support. Second, the effects are muted in countries where non-green (dirty) energy is a relatively small input into production. Third, political costs are not significant when CCPs are implemented during periods of low oil prices, generous social insurance and low inequality.

Influence of Economic and Political Variables on the Mortality Rate per Covid-19

Purpose: The global pandemic caused by Covid-19 has caused millions of infected and dead people around the world, generating an unprecedented crisis, and affecting the economy of all countries. The aim of this work is to analyze whether some of the most influential and determining variables of the economic level and welfare of a country, such as GDP, HDI or public debt, have a significant influence on the coronavirus mortality rate in the countries most affected by the pandemic. It is also examined whether the level of policies adopted as measured by the Government Response Stringency Index influences this rate. Design/Methodology/Approach: The countries with the highest number of Covid-19 cases at the beginning of the year 2021 were taken into account and the variables to be analyzed for these countries were obtained from different databases. A multiple regression was performed to determine whether the variables mentioned above have an influence on the mortality rate due to Covid-19. Findings: The results obtained show that GDP per capita has a positive influence and that lower public debt (expressed as a percentage of GDP) can contribute to reducing the number of deaths. In addition, the level of policies adopted by countries has a significant influence. The more restrictive these policies are, the lower the number of deaths per 100,000 inhabitants. This demonstrates the effectiveness of certain measures such as school closures or border closures. Practical Implications: the article provides recommendations on politics and Covid-19. It will be necessary to continue in this line of measures and propose a series of policies, such as mobility restrictions, perimeter confinements or mandatory teleworking or online teaching, to reduce the total number of deaths due to coronavirus and start economic reconstruction. Originality/Value: Significant findings on economic and political variables affecting Covid-19 mortality have been found.

Does a Government Response to COVID-19 Hurt the Stock Price of an Energy Enterprise?

ABSTRACT This research examines the shock of a government response to COVID-19 on the stock prices of 30 international energy enterprises spanning from January 1, 2020 to December 31, 2020. Overall, the empirical results denote that a government response stringency index, containment and health index, and economic support index all have a statistically significant negative impact on their stock prices. The negative impact from the containment and health index is especially the greatest, implying that a government’s stringent responses have great negative effect on the stock prices of most energy enterprises.

Functional cluster and canonical correlation analysis of EU countries by number of daily deaths and stringency index during Covid-19 pandemic

The danger of a global pandemic, such as the new Coronavirus (Covid-19),is obvious. This study aims to investigate the behavior and relationship of thenumber of daily new conrmed deaths per million and the stringency indexof twenty-seven European Union (EU) countries by utilizing functional clusteranalysis and functional canonical correlation analysis. Functional clusteranalysis was used to observe how countries cluster together according to dailydeaths during the time interval between March and July 2020. Functionalcanonical correlation analysis was also utilized to measure the correlationbetween the frequency index and daily deaths, and also to determine therelative positions of countries concerning their respective variability structure.The data is obtained from OWID. Here, it is seen that Italy, Spain,Belgium, and France are particularly aected by the pandemic during thetime interval within the EU countries, and the course of daily deaths is in adierent position compared to other EU countries. At the same time, a veryhigh relationship emerged between the stringency index and daily deaths asexpected.

Correlation of Society Stringency/Openness Measures with Timely Trend of COVID-19 Cases - Case Study – Albania Versus Italy

Following the WHO declaration for COVID-19 as a pandemic, this disease has caused an international crisis with a severe impact on economic and health care systems. After the first cases reported in China, the disease has progressively widespread worldwide where all affected countries has adapted specific safety protocols and tried to find new therapies in order to face this new disease. The final objective is to evaluate the overall impact of stringency measures taken from Albanian and Italian governmental authorities in relation to daily cases of COVID-19 in each country. This is a descriptive paper where the data collected in Albania and Italy according to authority-based stringency measures were compared with their output; temporary trend of daily cases. Officially Albania reported the first COVID-19 case in 8th of March in an Albanian citizen just arrived from Italy, meanwhile in Italy the first cases dated January 31st were a Chinese couple in Rome who had travel from Wuhan City. Both governments took action by applying different closer measures; thereby Blavatnik School of Government has introduced the Oxford COVID-19 Government Response Tracker (OxCGRT) in order to quantifying numerically these actions. According to OxCGRT, Albania and Italy present similar level of stringency indicator, but epidemiological curve of daily cases is totally different in shape. Italian curve corresponds to a typical outbreak, while Albania curve seem like any endemic disease in the population. In front of this pandemic, the Albanian curve might be cut off as a result a small number of tests carried out by Albania authorities, 6906 tests/million population, which is far lower than tests performed by Italian authorities, which is 11 times fold (79908 test/million population). Toward end of May the number of COVID-19 were dropped so both governments planned to relief closure measures by opening most of public and economic activities. In front of the fear that COVID-19 could rise up again, as a result of virus transmission amongst people that was observed in Albania, while in Italy continued with the low and decreasing trend of disease cases. Data and their comparison though different indicators or index, shows that stringency measures could contribute on a temporary diminution of new cases of COVD-19, but if not are accompanied with individual protection measures, and/or special vulnerable groups it might be a missing opportunity because the general population might lose what was achieved during national quarantine. On the other hand, taking in consideration low public spending per capita in Albania (307 USD, 2014), moving from “hummer” toward “dance” phase, managing and supporting health system is critical. Public health services should be mainly the entitled authorities to monitor data and come up with specific and efficient measures in order to prevent an increase of cases on the overall population.Keywords: Albania, Italy, stringency index, epidemic, COVID-19; Pandemic;