Assessing independence of two series was, is and will be the most fundamental goal of econometric/economic practitioners. Most of the economic (especially macroeconomic) data are time dependent and economists are interested to validate different economic theories using sophisticated data analysis tools. At the end of 19th century Karl Pearson developed coefficient of correlation to assess correlation between two series, however Yule (1926) criticized the use of this coefficient for time series because of its autocorrelated behavior. Haugh (1976) was considered the first to propose a measure of correlation for time series. His idea of pre-whitening the series’ first and then apply correlation coefficient became very famous. Since then different versions of Haugh test were developed. The latest version was developed by Rehman and Malik (2014) that is also based on the same idea, however, the pre-whitening process became more refined and modified. For the last four decades, several tests of independence for time series are developed. Every test was developed on a particular property of underlying assumptions, and it works in its own domain and fails to work in other situations/domains. Researchers working in this area, presented few results to compare proposed test and one or two previously developed tests and concluded superiority of his/her proposed test. These studies include Hong (1996b), Duchesne and Roy (2003a) and so on. These comparisons are ad hoc in nature and no comprehensive study available in the literature to unfold the strengths and weaknesses of these tests. We organized a comprehensive Monte Carlo simulation study to compare tests of independence and selected a broad data generating process in a common framework. For this purpose, standard stringency criteria of Zaman (1996) has been used. In presented study, we have selected eleven available tests of independence for time series. To use stringency criteria, tests of independence should maintain a stable size and then based on its powers we can decide about the appropriateness of the test. For checking the size stability of the tests, we have used twenty-one different specifications of stochastic part in our data generating process, similarly two specifications of deterministic part have been used for each stochastic part case. So, we have tested size stability at total forty-two different combinations of data generating process. Simulated critical values were used, as past studies suggested that asymptotic critical values are not appropriate. All eleven tests of independence have their sizes around nominal size of 5%. Following the stable size, power analysis has been carried out for the same combinations of the data generating process and results suggests that Atiq test performs well in small sample size in almost all cases. PhamRoy test remains on second position in small samples but in many situations, it supersedes Atiq test in medium and large sample sizes. Haugh test remains at third place in almost all cases of the simulation study however the difference between the shortcomings of Haugh and PhamRoy test are very large. The remaining tests have not shown any considerable performance. Kim Lee, LiHui and Bouhaddioui tests considered worst in all sample sizes and with and without deterministic part cases. Another important contribution of this study is to compare three important techniques used to check the dependence of two or more-time series, these include cointegration, Granger causality and Tests of independence for time series. Using renowned Keynesian function of income and consumption, we applied these tests on real economic data of income and consumption of 100 countries from 1970 to 2014. The results depict that three selected tests of independence, i.e. Atiq, PhamRoy and Haugh tests have appreciable power gains and lower size distortions. Again, it is observed that Atiq test shows better empirical power gain with least size distortion. PhamRoy and Haugh tests also shows good performance, have good power gains and small size distortions. However, five cointegration tests which are considered relatively better by Khan (2017) and famous Granger causality test have shown very poor performance both in terms of real empirical size and power.
"پھر کیا تم لوگوں نے یہ سمجھ رکھا ہے کہ یونہی جنت کا داخلہ تمہیں مل جائے گا، حالانکہ ابھی تم پر وہ سب کچھ نہیں گزرا ہے، جو تم سے پہلے ایمان لانے والوں پر گزر چکا ہے؟ اُن پر سختیاں گزریں، مصیبتیں آئیں، ہلا مارے گئے، حتیٰ کہ وقت کارسول اور اس کے ساتھی اہل ایمان چیخ اٹھے کہ اللہ کی مدد کب آئے گی اُس وقت انہیں تسلی دی گئی کہ ہاں اللہ کی مدد قریب ہے"۔
The Corona virus (SARS-CoV2) pandemic initiated in late December 2019 in Wuhan city of Hubei, China, which has rapidly progressedinvolving more than 215 countries of the world. It was caused by novel SARS-COV2 coronavirus with Huanan seafood wholesale market as the possible point of origin. In past two decades, coronaviruses epidemic of Middle East Respiratory Syndrome (MERS-COV) had 37% mortality rate and Severe Acute Respiratory Syndrome (SARS-COV) had 10% affecting more than 10,000 population together. World Health Organization (WHO) declared it as the sixth Public Health Emergency of International Concern (PHEIC) on January 30, 2020 and later on March 11, 2020, the WHO labeledit as a pandemic. The first case of COVID-19 from Pakistan was reported on 26th February, 2020 and has affected over 354,000 people with a mortality of over 7000 patients. Many countries of the world have seen second wave of this pandemic. Government ofPakistan has also declared a second waveon October 28, 2020, after the rise in cases from 500 to 750 per day. Now it has crossed 2000 cases. The data released by the National Command and Operation Centre (NCOC) shows that the current percent positivity rate is close to 3 compared to the previous figure of lesser than 2 making it a bigger challenge than first wave in Pakistan. The patients now presenting in hospitals are all in critical condition. Lack of a specific vaccine or antiviral drug and non-compliance to the standard preventive measures is the major reason of initiation of a second wave of this viral infection in Pakistan. Being a nation we need to be responsible. Our country may go into economic crisis & our health facilities may choke. We have to understand how to live with this virus till the availability of vaccine or Curative antiviral drug. TheGovernment of Pakistan is creating awareness in the public for the second wave as the situation of pandemic is getting worse. Smart lock downs are being implementedbut people are not following preventive measures that are leading to infection spread at a very alarming speed. At the moment preventive measures are the only way to stop the spread of disease. Preventive measures should be adopted to contain this deadly disease. Wearing masks, using hand sanitizers, washing hands with soap for 20 sec, keeping social distance of 6 feet are mandatory preventive strategies. Social, political, business, recreationaland religious gatherings, should be avoided. Educational institutesshould follow strict standard operating procedures. Most of the people in Pakistan are not considering this disease as a matter of serious concern due to unawareness, poverty, beliefs and lack of resources. People should ignore such disbeliefs and should start considering it as a great health concern. They should follow the preventive measures in true sense.
During the Avian Influenza (AI) outbreaks in different areas of Pakistan (2003 - 06), a number of Avian Influenza Virus (AIV) isolates were recovered from the clinical samples. The samples were subjected to comparative diagnostic evaluation using in-ovo propagation, Virus Neutralization Test (VNT), rapid detection kits and Reverse Transcriptase- Polymerase Chain Reaction (RT-PCR). The data revealed that RT-PCR technique was most sensitive and specific for the detection of Avian Influenza Virus subtypes and for differentially diagnosing it from other avian respiratory pathogenic viruses. These isolates were further utilized for the development of multiplex RT-PCR. A multiplex reverse transcriptase polymerase chain reaction (mRT-PCR) was developed and standardized for the detection of type A influenza viruses, Avian Influenza Virus (AIV) subtype H7, H9 and H5 haemagglutinin gene with simultaneous detection of 3 other poultry respiratory pathogens Newcastle disease virus (NDV), infectious bronchitis virus (IBV) and infectious laryngotracheitis virus (ILTV). Seven sets of specific oligonucleotide primers were used in this study for the M-Gene of AIV and haemagglutinin gene of subtypes of H7, H9 and H5 of AIV. Three sets of other specific oligonucleotide primers were used for the detection of avian respiratory pathogens other than AIV. The mRT-PCR DNA products were visualized by Agarose Gel Electrophoresis and consisted of DNA fragments of 1023bp for M-Gene of AIV, 149bp for IBV, 320bp for NDV and 647bp for ILTV. The second set of primers used for m-RT-PCR of H7N3, H9N2 and H5N1 provided DNA products of 300bp for H7, 456bp for H5 and 808bp for H9. The mRT-PCR products for the third format consisted of DNA fragments of 149bp for IBV, 320bp for NDV, 647bp for ILTV, 300bp for H7, 456bp for H5, 808bp for H9. The sensitivity and specificity of mRT-PCR was determined and the test was found to be sensitive and specific for the detection of AIV and other poultry respiratory pathogens. In the present study, multiplex PCR technique has been developed to simultaneously detect and differentiate three most important subtypes of AIV’s alongwith 3 most common avian respiratory pathogens prevalent in poultry in Pakistan. The non-structural 1 (NS1) protein of avian influenza viruses has been earlier described as a remarkably conserved protein amongst type A influenza viruses, however with xxivsubsequent findings of is truncation during extensive circulation in poultry has led to further investigate its mutation in association with point mutations simultaneously occurring in more variable genes such as HA and NA. Apart from affecting any of the biological functions of these viruses, these mutations may affect the immunogenic component(s) of these viruses, affecting the efficacy of prevalent vaccines. To establish if Pakistani H7N3 Avian influenza viruses undergo any truncation in non-structural genes, the non-structural gene 1 (NS1) of 22 H7N3 Avian influenza A viruses isolated from commercial and domestic poultry was sequenced and compared phylogenetically. The isolates included in the present study were both of low pathogenecity (LPAI) and highly pathogenic strains (HPAI) of H7N3 avian influenza viruses as observed in the field with regards to their mortality rates. These isolates circulated in N.W.F.P, Punjab, and Sindh areas of Pakistan from 1995 to 2005. Size variation in the predicted amino acid sequence of each NS1 was revealed with two different levels of carboxy-terminal truncation in those isolates. Of the 22 isolates analyzed, 02 isolates A/Chicken/Pakistan/NARC-100/04 and A/Chicken/Pakistan/NARC-1282/04 encoded a full length NS1 protein of 230 amino acids, whereas 20 encoded a truncated protein of 217 amino acids. The isolates exhibiting the truncated carboxy terminal NS1 protein, clustered together and appeared to be closest to A/Duck/Jiang Xi/6146/03 (H5N3), A/Duck/Hong Kong/610/79 (H9N2) and A/Aquatic Bird/Korea/CN-1/04 (H3N6) at the nucleotide level and amino acid level. In contrast, the nucleotide sequence of one of the isolates with the full length NS1 protein (A/Chicken/Pakistan/NARC-1282/04) showed 99.9% nucleotide homology and 99.6% homology to a set of Italian H7N3 isolates of Turkey from 2002 at the NS1 gene e.g A/turkey/Italy/8912/2002(H7N3) and A/turkey/Italy/214845/02(H7N3). The other isolate (A/Chicken/Pakistan/NARC-100/04) with the full length NS1 protein showed the highest homology (96%) with the NS1 gene of an H5N7 subtype virus A/mallard/Denmark/64650/03. Out of these 22 H7N3 isolates sequenced for the NS1 gene, 6 isolates from the Northern Parts of Pakistan were further sequenced for the HA and NA genes. One of the isolates had an untruncated NS1 whereas 5 were truncated. The 5 H7N3 isolates with truncated NS1 sequenced were HPAI, for the HA gene and showed the presence of typical highly pathogenic pattern of deduced amino acid sequence at the HA cleavage site. The xxvphylogenetic analysis of these H7N3 isolates indicated a close resemblance to other Pakistani isolate sequences in the GenBank, with the next closest resemblance to the H7N3 isolate from a Peregrine Falcon in U.A.E in the GenBank besides the other Pakistani isolates. The untruncated isolate for the NS1 gene, A/Chicken/Pakistan/NARC- 1282/04, showed a typical low pathogenicity cleavage site sequence at the HA cleavage site. Phylogenetic Analysis of this isolate indicated a close resemblance to Italian H7N3 isolates especially A/Chicken/Italy/682/2003 (H7N3) and A/turkey/Italy/8535/2002 (H7N3). The NA gene was analyzed for the presence or absence of a stalk region in the isolates sequenced. The 5 truncated H7N3 isolates for the NS1 Gene and HP for HA gene had a stalked NA protein as in H7N3 isolates reported in wild birds showing a close resemblance to other previously sequenced H7N3 Pakistani isolate sequences in the GenBank, whereas the untruncated NS1 H7N3 isolate also showing a LPAI cleavage site sequence A/Chicken/Pakistan/NARC-1282/04 had a deleted NA stalk region, deduced amino acid sequence showing a deletion of 24 amino acids in concordance with other Italian H7N3 isolates reflecting a probable introduction of a highly circulating virus in domestic poultry. It was concluded from the present study that the H7N3 isolates from Pakistan show slow antigenic drift and continue to evolve in a slow manner during a ten year period in the poultry population. With information obtained from the data on NS1, HA1 and NA, continuous monitoring of circulating viruses is possible and subsequent production of homologous vaccines from field strains is key to the control of HPAI in poultry.