Displaying all 7 publications

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  1. AUDY JR
    Med J Malaya, 1956 Sep;11(1):21-32.
    PMID: 13399540
    Matched MeSH terms: Communicable Diseases/transmission*
  2. WELLS CW
    Med J Malaya, 1956 Sep;11(1):71-5.
    PMID: 13399544
    Matched MeSH terms: Communicable Diseases/transmission*
  3. Apenteng OO, Ismail NA
    PLoS One, 2014;9(6):e98288.
    PMID: 24911023 DOI: 10.1371/journal.pone.0098288
    Previous models of disease spread involving delay have used basic SIR (susceptible--infectious--recovery) formulae and approaches. This paper demonstrates how time-varying SEIRS (S--exposed--I - R - S) models can be extended with delay to produce wave propagations that simulate periodic wave fronts of disease spread in the context of population movements. The model also takes into account the natural mortality associated with the disease spread. Understanding the delay of an infectious disease is critical when attempting to predict where and how fast the disease will propagate. We use cellular automata to model the delay and its effect on the spread of infectious diseases where population movement occurs. We illustrate an approach using wavelet transform analysis to understand the impact of the delay on the spread of infectious diseases. The results indicate that including delay provides novel ways to understand the effects of migration and population movement on disease spread.
    Matched MeSH terms: Communicable Diseases/transmission
  4. Chen PC
    Med J Malaysia, 1977 Dec;32(2):100-2.
    PMID: 614474
    Matched MeSH terms: Communicable Diseases/transmission
  5. Law KB, M Peariasamy K, Mohd Ibrahim H, Abdullah NH
    Sci Rep, 2021 10 18;11(1):20574.
    PMID: 34663839 DOI: 10.1038/s41598-021-00013-2
    The conventional susceptible-infectious-recovered (SIR) model tends to magnify the transmission dynamics of infectious diseases, and thus the estimated total infections and immunized population may be higher than the threshold required for infection control and eradication. The study developed a new SIR framework that allows the transmission rate of infectious diseases to decline along with the reduced risk of contact infection to overcome the limitations of the conventional SIR model. Two new SIR models were formulated to mimic the declining transmission rate of infectious diseases at different stages of transmission. Model A utilized the declining transmission rate along with the reduced risk of contact infection following infection, while Model B incorporated the declining transmission rate following recovery. Both new models and the conventional SIR model were then used to simulate an infectious disease with a basic reproduction number (r0) of 3.0 and a herd immunity threshold (HIT) of 0.667 with and without vaccination. Outcomes of simulations were assessed at the time when the total immunized population reached the level predicted by the HIT, and at the end of simulations. Further, all three models were used to simulate the transmission dynamics of seasonal influenza in the United States and disease burdens were projected and compared with estimates from the Centers for Disease Control and Prevention. For the simulated infectious disease, in the initial phase of the outbreak, all three models performed expectedly when the sizes of infectious and recovered populations were relatively small. As the infectious population increased, the conventional SIR model appeared to overestimate the infections even when the HIT was achieved in all scenarios with and without vaccination. For the same scenario, Model A appeared to attain the level predicted by the HIT and in comparison, Model B projected the infectious disease to be controlled at the level predicted by the HIT only at high vaccination rates. For infectious diseases with high r0, and at low vaccination rates, the level at which the infectious disease was controlled cannot be accurately predicted by the current theorem. Transmission dynamics of infectious diseases with herd immunity can be accurately modelled by allowing the transmission rate of infectious diseases to decline along with the reduction of contact infection risk after recovery or vaccination. Model B provides a credible framework for modelling infectious diseases with herd immunity in a randomly mixed population.
    Matched MeSH terms: Communicable Diseases/transmission*
  6. Sekarajasekaran IA
    PMID: 538513
    Development of a human community are not without changes in its environment. Such changes result in either beneficial or adverse effects on human health. In Malaysia, in the wake of the New Economic Policy aimed at the redressing of the poor population and income distribution, development of the nation has brought about various changes in the environment. Some of these changes have elevated basic public health problems, while others, particularly new agricultural practices and industrialisation programmes with urbanisation trends, have brought a new set of problems due to water pollution and sanitation. Various measures are being taken to protect and to improve the environment so that progress can be realised with minimum adverse effects. This also calls for assistance from international sources, in terms of expertise, training and funds.
    Matched MeSH terms: Communicable Diseases/transmission
  7. Lam SK
    Emerg Infect Dis, 1998 Apr-Jun;4(2):145-7.
    PMID: 9621184
    Matched MeSH terms: Communicable Diseases/transmission
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