XVII.—Tests for Randomness in a Series of Numerical Observations

Kermack, W. O.; McKendrick, A. G.

doi:10.1017/s0370164600013778

Cited by 99 publications

(120 citation statements)

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“…. The study conducted by Kermack and McKendrick (1927) for treatment of the Bombay plague of 1905-06 proved the capability of mathematical models in understanding and predicting epidemics. Anderson and May (1991) present more models of infections including HIV with illustrations.…”

Section: Resultsmentioning

confidence: 99%

Statistical Modelling and Forecasting of Reported HIV Cases in Nepal

Sathian

Sreedharan

Mittal

et al. 1970

Nepal J Epidemiology

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BackgroundThe real state about the spread of the HIV epidemic in Nepal is not clear since the details available are on the basis of risk group. The objective of the study is to extract as much as information possible from available data and find out the trends of HIV cases in future.Material and methodsA retrospective study was carried out on the data collected from the Health ministry records of Nepal, between 1988 and 2004. Descriptive statistics and statistical modelling were used for the analysis and forecasting of data.Results Excluding the constant term from the equation, the cubic model was the best fit, for the forecasting of HIV cases(p=0.001). NCASC reported cumulative number of HIV cases up to 2009 differs from our projected cases by 46 (99.99% accuracy in prediction). Using cubic equation, it is estimated that 4773 males, 2163 females and 6936 total reported number of HIV cases will be there in Nepal by the year 2015.ConclusionThe HIV cases in Nepal are having an increasing trend. Estimates of the total number of prevalent HIV infections attributable to the major routes of infection make an important contribution to public health policy. They can be used for the planning of healthcare services and for contributing to estimates of the future numbers with severe HIV infection used for planning health promotion programmes.Key words: Statistical Modelling; HIV; NepalDOI: http://dx.doi.org/10.3126/nje.v1i3.5570Nepal Journal of Epidemiology 2011;1(3) 106-110

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Section: Resultsmentioning

confidence: 99%

Statistical Modelling and Forecasting of Reported HIV Cases in Nepal

Sathian

Sreedharan

Mittal

et al. 1970

Nepal J Epidemiology

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“…We are not prepared to stress these facts. Quite apart from the mere sampling errors of the rates at higher ages, we find that the total numbers of maxima in the first 50 days of each experience do not appreciably differ from those to be expected in a random succession (see Kermack & McKendrick, 1937), but we are inclined to believe that there may be a real tendency to maxima at intervals of some 10 days in later herd experience and a quite unmistakable tendency to a first maximum near the 15th day of herd life.…”

Section: -2 125mentioning

confidence: 68%

The effect of withdrawing mice from an infected herd at varying intervals

Greenwood

Hill

Topley

et al. 1939

J. Hyg.

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(Greenwood et al. 1936) we included a short discussion of the few scattered observations that we had made on the effects of the dispersal of an infected herd (pp. 189-92). Briefly, we had found that the division of a herd, in which an epidemic due to Bact. typhi-murium was under way, into small isolated groups was followed by a greatly decreased rate of mortality in those groups when the dispersal was carried out at the beginning of the epidemic period. Reaggregation of the groups resulted in a fresh spread of the disease, but the final mortality was lower than in a similar herd which had not been dispersed during the earlier stages of cage life (Topley, 1922). In a subsequent experiment (Topley & Wilson, 1925) dispersal was carried out at a later stage of epidemic spread, and very different results were obtained. For the first three weeks or so after division into small groups there was no material difference between the mortality experienced by the dispersed and notdispersed mice. But at about the 25th day the death-rate in each of the dispersed herds showed a definite decline, while that in the undispersed herds continued unabated for some further length of time.These few observations were clearly in conformity with the reasonable view that the conditions of contact may exert an exceedingly important effect on epidemic spread, but that to secure much benefit from dispersal it must be carried out shortly after exposure to risk in the epidemic environment.In the same report we discussed at some length (e.g. pp. 94 et seq.) the general problem of the evolution of disease in a herd, and pointed out that a fundamental datum was knowledge of the time relation of the process of infection. Our most precise, but limited, data (Topley et at. 1924) showed that by the end of 25 days about four-fifths of animals exposed to risk in an infected herd in which mouse typhoid was spreading had given proof of infection.Another question intimately related to this is the relative resistance to infection of mice which have survived exposure to the environment for varying lengths of time. All our previous experiments have shown quite clearly that the average resistance of surviving mice increases with increasing

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“…For a review of some related literature, see the introduction to [12]; the authors trace the beginning of this line of research to the nineteenth century. However, the research in this area seems to have a number of separate lines, because the authors of [12] do not cite [10] or [26,27]. In view of this disconnected nature of the literature we are not sure whether we were able to trace all the existing results in the area that are relevant to our paper.…”

Section: Meteor Craters In Circular Graphsmentioning

confidence: 94%