Given the high level of global mobility, pandemics are likely to be more frequent, and with potentially devastating consequences for our way of life. With COVID-19, Australia is in relatively better shape than most other countries and is generally regarded as having managed the pandemic well. That said, we believe there is a critical need to start the process of learning from this pandemic to improve the quantitative information and related advice provided to policy makers. A dispassionate assessment of Australia’s health and economic response to the COVID-19 pandemic reveals some important inadequacies in the data, statistical analysis and interpretation used to guide Australia’s preparations and actions. For example, one key shortcoming has been the lack of data to obtain an early understanding of the extent of asymptomatic and mildly symptomatic cases or the differences across age groups, occupations or ethnic groups. Minimising the combined health, social and economic impacts of a novel virus depends critically on ongoing acquisition, integration, analysis, interpretation and presentation of a variety of data streams to inform the development, execution and monitoring of appropriate strategies. The article captures the essential quantitative components of such an approach for each of the four basic phases, from initial detection to post-pandemic. It also outlines the critical steps in each stage to enable policy makers to deal more efficiently and effectively with future such events, thus enhancing both the social and the economic welfare of its people. Although written in an Australian context, we believe most elements would apply to other countries as well.

Coronavirus disease 2019 (COVID-19) has been considered a global threat spreading to Nigeria and posing major public health threats and concerns. This led to the introduction of internationally acceptable non-pharmaceutical interventions (NPI) such as lockdowns, social distancing, and mandatory use of face masks by the Nigerian government to curtail the disease. This study aims to develop an Autoregressive Integrated Moving Average (ARIMA) model to predict COVID-19 cases vis Total Confirmed Cases (TCC) and Total Discharged Cases (TDC) in Nigeria based on the daily data obtained from the Nigeria Centre for Diseases Control (NCDC) from 27th February 2020 to 6th June 2020. The autocorrelation function (ACF), and partial autocorrelation function (PACF) were used to determine the constructed model. An ARIMA model was developed to predict the trend of TCC and TDC for the next 200 days. Forecasting was done using the constructed models. The finding shown that TCC increased to 50,225 with a CI between 29,425 to 100,450 and TDC to 20,186 with CI between 11,106 to 40,366 approximately. The result shows a significant increase in both TCC and TDC from COVID-19 which should guide the government roll out and management of the different NPI and policies to contain the virus.

Statistics Indonesia (BPS) carries out a population census every ten years, which provides data on the number, composition, distribution, and characteristics of the Indonesian population. However, the vast geographical area and the variety of tribal communities pose a challenge in adequately conducting this activity. Over the years, BPS has taken various ways to minimize under coverage issues, such as digitizing, tagging, and categorizing each region into local environmental units (SLS) or non-SLS. The population census is carried out in areas categorized as SLS because non-SLS are uninhabited. However, in reality, there are conditions where the non-SLS areas, such as inland and forest, are inhibited by tribal people, thereby making it difficult for enumerators to collect data. Therefore, this study proposes an innovative approach to underpin the population census in non-SLS areas using a building detection approach. It is assumed that the presence of a building in an area indicates the possible presence of people living there. The research developed nine CNN models with U-Net architecture capable of performing semantic segmentation to detect buildings on Sentinel 2 and Landsat 8 satellite imagery using some bands. The result showed that the U-Net architecture is the best model with input dimensions of N × 64 × 64 × 10, where N is the amount of data capable of detecting buildings in Sentinel 2 satellite images. Furthermore, the model produces an F1 score on the validation data of 0.7282 and has been trained for 50 epochs (repetition) with a dashboard developed to visualize the detection results with a plugged-in model.

Most discussions on data quality overlook the legal framework within which data are collected. This paper examines India’s Census Act, 1948 that provides the legal framework for conducting population censuses. The Act stipulates punishment for interfering with the process of enumeration but the punitive provisions have not been invoked to deal with cases of widespread manipulation of census. Major instances of manipulation were reported in 1951 and 2001 after the government introduced additional punitive measures in 1948 and 1994, respectively. The paper identifies the structural flaws of the Census Act, 1948 vis-à-vis manipulation. It compares the Act with other Indian laws on the collection of statistics and census laws of other common law countries. It shows that fines are very small compared to per capita income in most countries and yet violators are rarely fined. The paper uses simple games to explain why the punitive provisions might be redundant and suggests that the problem of interference with government statistics can possibly be addressed without recourse to law. The insights drawn from the games are examined in light of the experience of Nagaland and other states of India where census statistics were affected by widespread manipulation in the recent decades.

Labour accounts provide an orderly manner to compare, reconcile and built-on data from different sources. The procedures may in principle be applied to topics other than labour. As part of a research project at the Statistical Research and Training Center of Iran, an attempt was made for the first time to develop labour accounts for Iran for the reference year 1390 (March 2011–February 2012). The procedure is described in the first part of the paper. In the second part, comparative tables for Australia, Denmark and Iran are constructed on each of the three main elements (employment, hours of work and employment-related income). In the process of comparing the data, the differences in the underlying methodologies are reviewed and an assessment is made on the way forward. To date, very few countries have attempted to construct labour accounts. The three countries discussed in this paper represent a diverse set of statistical systems, but have in common, particularly, Iran and Australia, the same concepts and definitions in line with the ILO international standards.