Using uniformat and gene[rate] to Analyze Data with Ambiguities in Population Genetics

Some genetic systems frequently present ambiguous data that cannot be straightforwardly analyzed with common methods of population genetics. Two possibilities arise to analyze such data: one is the arbitrary simplification of the data and the other is the development of methods adapted to such ambiguous data. In this article, we present an attempt at such a development, the uniformat grammar and The gene[rate] tools, highlighting the specific aspects and the adaptations required to analyze ambiguous nominal data in population genetics.

Can we rely on COVID-19 data? An assessment of data from over 200 countries worldwide

To fight COVID-19, global access to reliable data is vital. Given the rapid acceleration of new cases and the common sense of global urgency, COVID-19 is subject to thorough measurement on a country-by-country basis. The world is witnessing an increasing demand for reliable data and impactful information on the novel disease. Can we trust the data on the COVID-19 spread worldwide? This study aims to assess the reliability of COVID-19 global data as disclosed by local authorities in 202 countries. It is commonly accepted that the frequency distribution of leading digits of COVID-19 data shall comply with Benford's law. In this context, the author collected and statistically assessed 106,274 records of daily infections, deaths, and tests around the world. The analysis of worldwide data suggests good agreement between theory and reported incidents. Approximately 69% of countries worldwide show some deviations from Benford's law. The author found that records of daily infections, deaths, and tests from 28% of countries adhered well to the anticipated frequency of first digits. By contrast, six countries disclosed pandemic data that do not comply with the first-digit law. With over 82 million citizens, Germany publishes the most reliable records on the COVID-19 spread. In contrast, the Islamic Republic of Iran provides by far the most non-compliant data. The author concludes that inconsistencies with Benford's law might be a strong indicator of artificially fabricated data on the spread of SARS-CoV-2 by local authorities. Partially consistent with prior research, the United States, Germany, France, Australia, Japan, and China reveal data that satisfies Benford's law. Unification of reporting procedures and policies globally could improve the quality of data and thus the fight against the deadly virus.

Understanding reasons for and strategic responses to administrative health data misreporting in an Indian state

The misreporting of administrative health data creates an inequitable distribution of scarce health resources and weakens transparency and accountability within health systems. In the mid-2010s, an Indian state introduced a district ranking system to monitor the monthly performance of health programmes alongside a set of data quality initiatives. However, questions remain about the role of data manipulation in compromising the accuracy of data available for decision-making. We used qualitative approaches to examine the opportunities, pressures and rationalization of potential data manipulation. Using purposive sampling, we interviewed 48 district-level respondents from high-, middle- and low-ranked districts and 35 division- and state-level officials, all of whom had data-related or programme monitoring responsibilities. Additionally, we observed 14 district-level meetings where administrative data were reviewed. District respondents reported that the quality of administrative data was sometimes compromised to achieve top district rankings. The pressure to exaggerate progress was a symptom of the broader system for assessing health performance that was often viewed as punitive and where district- and state-level superiors were viewed as having limited ability to ensure accountability for data quality. However, district respondents described being held accountable for results despite lacking the adequate capacity to deliver on them. Many rationalized data manipulation to cope with high pressures, to safeguard their jobs and, in some cases, for personal financial gain. Moreover, because data manipulation was viewed as a socially acceptable practice, ethical arguments against it were less effective. Potential entry points to mitigate data manipulation include (1) changing the incentive structures to place equal emphasis on the quality of data informing the performance data (e.g. district rankings), (2) strengthening checks and balances to reinforce the integrity of data-related processes within districts and (3) implementing policies to make data manipulation an unacceptable anomaly rather than a norm.

Effective Recruitment or Bot Attack? The Challenge of Internet-Based Research Surveys and Recommendations to Reduce Risk and Improve Robustness

Internet-based research has exploded in popularity in recent years, enabling researchers to offer both investigations and interventions to broader participant populations than ever before. However, challenges associated with internet-based research have also increased-notably, difficulties verifying participant data and deliberate data manipulation by bot and spam responses. This study presents a viewpoint based on 2 case studies where internet-based research was affected by bot and spam attacks. We aim to share the learnings from these experiences with recommendations for future research practice that may reduce the likelihood or impact of future attacks. The screening and verification processes used are presented and discussed, including the limitations of these. Based on our experience, security and screening within internet-based research platforms are partly effective, but no solution is available to protect researchers completely against bot attacks. Implications for future research and advice for health researchers are discussed.

Common data manipulations with R in biological researches

R is a computer language and has been widely used in science community due to the powerful capability in data analysis and visualization; and these functions are mainly provided by the developed packages. Because every package has strict format definitions on the inputted data, it is always required to appropriately manipulate the original data in advance. Unfortunately, users, especially for the beginners, are always confused by the extreme flexibility with R in data manipulation. In the present paper, we roughly categorize the common manipulations with R for biological data into four classes, including overview of data, transformation, summarization, and reshaping. Subsequently, these manipulations are exemplified in a sample data of clinical records of diabetic patients. Our main purpose is to provide a better landscape on the data manipulation with R and hence facilitate the practical applications in biological researches.

Predictive Analytics with Strategically Missing Data

We study strategically missing data problems in predictive analytics with regression. In many real-world situations, such as financial reporting, college admission, job application, and marketing advertisement, data providers often conceal certain information on purpose in order to gain a favorable outcome. It is important for the decision-maker to have a mechanism to deal with such strategic behaviors. We propose a novel approach to handle strategically missing data in regression prediction. The proposed method derives imputation values of strategically missing data based on the Support Vector Regression models. It provides incentives for the data providers to disclose their true information. We show that with the proposed method imputation errors for the missing values are minimized under some reasonable conditions. An experimental study on real-world data demonstrates the effectiveness of the proposed approach.

Regime Type and Data Manipulation: Evidence from the COVID-19 Pandemic

CONTEXT

This study examines whether autocratic governments are more likely than democratic governments to manipulate health data. The COVID-19 pandemic presents a unique opportunity for examining this question because of its global impact.

METHODS

Three distinct indicators of COVID-19 data manipulation were constructed for nearly all sovereign states. Each indicator was then regressed on democracy and controls for unintended misreporting. A machine learning approach was then used to determine whether any of the specific features of democracy are more predictive of manipulation.

FINDINGS

Democracy was found to be negatively associated with all three measures of manipulation, even after running a battery of robustness checks. Absence of opposition party autonomy and free and fair elections were found to be the most important predictors of deliberate undercounting.

CONCLUSIONS

The manipulation of data in autocracies denies citizens the opportunity to protect themselves against health risks, hinders the ability of international organizations and donors to identify effective policies, and makes it difficult for scholars to assess the impact of political institutions on population health. These findings suggest that health advocates and scholars should use alternative methods to estimate health outcomes in countries where opposition parties lack autonomy or must participate in uncompetitive elections.