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In South Asia, Pakistan is contributing to the highest childhood mortality, including infant mortality. Child mortality has been declined globally, but Pakistan is still struggling and far behind the targets of Sustainable Development Goals. There are several reasons behind high childhood mortality, including socioeconomic determinants and a lack of effective implementation of health-related policies, particularly in primary health care settings. In the present study, we use the stochastic univariate models to uncover the trend of infant mortality by using more than half of the century data from 1960-2017. The secondary data on infant mortality rate from 1960 to 2017 was extracted from World Bank Dataset. Descriptive and time series analysis were applied by using the programming language R. The median infant mortality rate was 109 per 1,000 live births, whereas a higher mortality rate (189.8/1000) was reported in 1960. A gradual decline in infant mortality rates is experienced every year. The mortality rate went down below 100/1,000 live births from 1993 and remained 61.2/1,000 in 2017. ARIMA (1, 2, 2) is an adequate forecasting model selected by using the Auto ARIMA function with a root mean square error is 0.1006. Five years ahead forecast is obtained and yielded that infant mortality rate is expected to remain in 2018 [59.54, C.I (59.34 - 59.73)], 2019 [57.93, C.I (57.47 - 58.40)], 2020 [56.39, C.I (55.48 - 57.30)], 2021 [54.90, C.I (53.35 - 56.45)] and 2022 [53.47, C.I (51.05 - 55.88)]. The decreasing trend is expected in the infant mortality rate in Pakistan. [ABSTRACT FROM AUTHOR]

BACKGROUND & OBJECTIVE: Statistical models play a significant role in understanding the trend, level and trajectory of infectious diseases and provide the foundation to formulate effective policies and timely intervention, so that the morbidity and mortality due to these diseases can be declined. This study aimed to uncover the trend and proposing a forecasting model for daily expected outbreaks due to COVID-19 of fourth spike in Pakistan. METHODOLOGY: This study is primarily based on a secondary data of COVID-19 daily confirmed outbreaks. The twomonth (1st June to 31st July 2021) time series data is recorded and available from COVID-19 health advisory platform by Ministry of National Health Services Regulation and Coordination official website. Descriptive and time series analysis (ARIMA, exponential smoothing models) were applied. The analysis was carried out using R programming language. RESULTS: The highest (5026) and the lowest (663), COVID-19 confirm cases reported on 31 July 2021 and 21 June 2021 respective, whereas the average confirmed cases were 1830 [762-2898] per day. Four different time series models are executed namely ARIMA, Brown, Holt and Winter. Among competitive models, ARIMA (0, 2, 1) is found to be an optimum forecasting model, selected by using auto ARIMA function with least root mean square error. A day ahead forecast is obtained under the selected ARIMA model and yielded that COVID-19 confirmed outbreaks is expected to increase about 3.1% per day. CONCLUSION: COVID-19 outbreaks are expected to rise in Pakistan and ARIMA (0, 2, 1) is an optimum forecasting model for daily COVID-19 outbreaks. [ABSTRACT FROM AUTHOR]