ANALYSIS OF COVID-19 CONTAMINATION AND DEATHS CASES IN BRAZIL ACCORDING TO THE NEWCOMB-BENFORD

The use of the Newcomb-Benford Law in assessing the quality of health and / orepidemiological information systems can allow relevant decisions to be made to improve these systems. In this context, this research aimed to carry out an assessment of the conformity of theinformation regarding the number of cases of contamination and deaths by COVID-19 in Brazil according to the Newcomb-Benford Law, from the moment of the occurrence of the first case of the disease and from the first death by COVID-19 in the country until the month of September 2020. With the aid of descriptive statistics and the use of metrics related to the Z test and themean absolute deviation it was possible to observe that, both from a national and longitudinal perspective as for the transversal-state perspective, the quantitative data referring to the cases of contamination by the coronavirus and the deaths that occurred as a result of COVID-19 did not present the expected behavior according to the Newcomb-Benford Law. Due to the lack of conformity in relation to the Newcomb-Benford Law, it is suspected that some level of conformity specific to this type of data has occurred, in the Brazilian context, since there are already studies that suggest the existence of proper levels of conformity for certain types of data.

Analyzing Benford’s Law’s Powerful Applications in Image Forensics

The Newcomb–Benford law states that in a set of natural numbers, the leading digit has a probability distribution that decays logarithmically. One of its major applications is the JPEG compression of images, a field of great interest for domains such as image forensics. In this article, we study JPEG compression from the point of view of Benford’s law. The article focuses on ways to detect fraudulent images and JPEG quality factors. Moreover, using the image’s luminance channel and JPEG coefficients, we describe a technique for determining the quality factor with which a JPEG image is compressed. The algorithm’s results are described in considerably more depth in the article’s final sections. Furthermore, the proposed idea is applicable to any procedure that involves the analysis of digital images and in which it is strongly suggested that the image authenticity be verified prior to beginning the analyzing process.

Using the Newcomb–Benford law to study the association between a country’s COVID-19 reporting accuracy and its development

The COVID-19 pandemic has spurred controversies related to whether countries manipulate reported data for political gains. We study the association between accuracy of reported COVID-19 data and developmental indicators. We use the Newcomb–Benford law (NBL) to gauge data accuracy. We run an OLS regression of an index constructed from developmental indicators (democracy level, gross domestic product per capita, healthcare expenditures, and universal healthcare coverage) on goodness-of-fit measures to the NBL. We find that countries with higher values of the developmental index are less likely to deviate from the Newcomb-Benford law. The relationship holds for the cumulative number of reported deaths and total cases but is more pronounced for the death toll. The findings are robust for second-digit tests and for a sub-sample of countries with regional data. The NBL provides a first screening for potential data manipulation during pandemics. Our study indicates that data from autocratic regimes and less developed countries should be treated with more caution. The paper further highlights the importance of independent surveillance data verification projects.

Newcomb-Benford law analysis on COVID-19 daily infection cases and deaths in Indonesia and Malaysia

Each country has been racing to contain the spread of COVID-19. The published data of daily infection and death cases can be used to measure the effectiveness of the control interventions. We focus our study in two Southeast Asia countries: Indonesia and Malaysia during period between March and November 2020. Newcomb-Benford law has been commonly used to analyze the probabilities of the first significant digits in natural occurrences since the late 19th century. It is a prominent statistical tool for its capability to detect frauds in datasets. A chi-squared test was recruited to quantify the closeness of the data and Newcomb-Benford law distributions. The results revealed that the distributions of daily infection and death cases in Indonesia followed Newcomb-Benford law while the opposite results were obtained for Malaysia. We have done the analysis of verifying the daily COVID-19 infection and death cases in Indonesia and Malaysia using Newcomb-Benford law. It can be inferred that, between March and November 2020, the control interventions in Indonesia was less effective compared to Malaysia.

Who Manipulates Data During Pandemics? Evidence From Newcomb-Benford Law

The COVID-19 pandemic has spurred controversies related to whether countries manipulate reported data for political gains. We study the association between accuracy of reported COVID-19 data and developmental indicators. We use the Newcomb-Benford law (NBL) to gauge data accuracy. We run an OLS regression of developmental indicators (EIU index, GDP per capita, healthcare expenditures, and universal healthcare coverage index) on goodness-of-fit measures to the NBL. We find that democratic countries, countries with the higher gross domestic product (GDP) per capita, higher healthcare expenditures, and better universal healthcare coverage are less likely to deviate from the Newcomb-Benford law. The relationship holds for the cumulative number of reported deaths and total cases but is more pronounced for the death toll. The findings are robust for second-digit tests, for a sub-sample of countries with regional data, and in relation to the previous swine flu (H1N1) 2009–2010 pandemic. The NBL provides a first screening for potential data manipulation during pandemics. Our study indicates that data from autocratic regimes and less developed countries should be treated with more caution. The paper further highlights the importance of independent surveillance data verification projects.JEL classification: F5, I10, I18, O1, O57, P52.

ASSESSING THE QUALITY OF COVID-19 DATA: EVIDENCE FROM NEWCOMB-BENFORD LAW

The COVID-19 infection started in Wuhan, China, spreading all over the world, creating global healthcare and economic crisis. Countries all over the world are fighting hard against this pandemic; however, there are doubts on the reported number of cases. In this paper Newcomb-Benford Law is used for the detection of possible false number of reported COVID-19 cases. The analysis, when all countries have been observed together, showed that there is a doubt that countries potentially falsify their data of new COVID-19 cases of infection intentionally. When the analysis was lowered on the individual country level, it was shown that most countries do not diminish their numbers of new COVID-19 cases deliberately. It was found that distributions of COVID-19 data for 15% to 19% of countries for the first digit analysis and 30% to 39% of countries for the last digit analysis do not conform with the Newcomb-Benford Law distribution. Further investigation should be made in this field in order to validate the results of this research. The results obtained from this paper can be important for economic and health policy makers in order to guide COVID-19 surveillance and implement public health policy measures.

Assessing the Efficacy of Budget in Controlling Fraudulent Personnel Cost Figure in Borno State Civil Service, Nigeria

The Borno State Government in its effort to rid the state civil service of the ghost worker syndrome made a lot of efforts such as verification of staff by consultants and physical head count of staff by committee. The present administration and its predecessors have all made that effort, but the syndrome seems far from over. It is in light of the above that this study seeks to investigate the Personnel Cost Budget of the state government in respect of education and health sectors to see if it assisted in controlling fraud in personnel cost. Both primary and secondary data were used for this research. Closed ended questionnaire was administered to personnel depart of the education and Health sectors, while the Borno state budget figure for these sectors are extracted from the State budget document of 2015 to 2019. These data were analysed by the use of Benford’s Law. The study found out that budget is being prepared annually on incremental basis using the previous year’s budget figure as basis. It also found out that the budgeted figure is always higher than the actual giving rise to favourable variance, and do not usual follow the pattern of Benford Law in which figures are supposed to appear in a numeric data setup in line with its rule.