So even provided that the- you are an expert of outbreak investigation, in many instances, the infectious disease spread may not be easily controlled. So suppose that major components of outbreak investigation are already surveyed and epidemic is confirmed, let's continue the desktop exercise. The epidemic appears to have been caused by a novel virus strain, say novel influenza virus. And due to absence of immunity in the population and rapid spread, it seems a major epidemic is unavoidable. So, the world will probably focus on your work in a matter of a week or two weeks. Then the immediate question that you have to handle is what kind of information you would like to investigate? So, this is a tough question, and when we are forced to face the forthcoming pandemic, we would like to enumerate factors that determines the disaster size of a pandemic. Namely, the total number of deaths that may occur in the population. The first component is transmissibility or the so-called transmission potential Of a novel infectious disease. Transmissibility tells you to what extent or, to what percentage the population will experience the infection. The second component is disease severity or clinical seriousness, which is usually measured by the risk of death, given infection. So it indicates how serious the infection is. The third component is the population characteristics. Especially in the case of influenza, it's important to measure the immunity level of the population. Because if the novel influenza virus strain is somewhat associated with a previously circulated influenza strain, A portion of the population might remain to be immune, Thereby decreasing the possible size of the pandemic. So using these three components, one could predict how severe the forthcoming pandemic will be. So from here, we would like to see how we actually measure these components. The first one is transmissibility. Transmissibility is evaluated using two different metrics. One is useful in a confined space, such as household or classroom in a primary school. And the measurement is referred to as secondary attack risk, which I am going to explain in the next slide. The alternative measurement is useful in an open space environment. And is referred to as basic reproduction number, which measures the infectiousness of a primary case to cause a secondary cases. So firstly, let’s have a look at the definition of the secondary attack risk, or the so called secondary infection risk if we measure this metric, using infected individuals. The secondary attack risk is defined as a proportion of individuals who became ill among all exposures. And calculated as a simple division of the number of cases who became ill among the total number of those who were exposed. And usually this is measured in a confined unit so that the calculation of this division becomes possible, namely the denominator, the number of exposed susceptible may be equated to the number of household members if all members are susceptible to a novel influenza virus. However one would want to exclude the individuals who are already immune prior to exposure. And therefore if a portion of the household members are immune prior to the household transmission event, we would have to be very careful with respect to the calculation of this division. So this table shows an example of the household secondary infection risk measured in Hong Kong, across a total of 94 households. So using RT-PCR, acute respiratory infection or influenza-like illness. So based on the different diagnostic criteria of influenza. The household secondary infection risk of pandemic 2009 was compared against that of seasonal influenza. As we observe here, they're using three different diagnostic methods. The estimated secondary infection risk of a pandemic influenza was comparable to seasonal influenza. Based on this type of observation, researchers were able to conclude that the transmissibility of pandemic influenza was comparable to seasonal influenza. The alternative measurement of infectiousness is referred to as basic reproduction number. It's defined and interpreted as the average number of secondary cases generated by a single primary case in a fully susceptible population. For instance, this figures shows the geometric growth of cases. So each circle represents an infected individual and each primary case, on average generates these two secondary cases, therefore R0 is two, okay. So if R0 is greater than one, probabilistically it can lead to a major epidemic. However if R0 is smaller than one, The emergence of cases can surely decline to extinction. So for that reason, the R0 has been empirically estimated for a number of diseases. In the case of influenza, R0 has been estimated to range from 1.2 to 2.0. In the case of smallpox, R0 was estimated to be 5. And in the case of measles, common disease, the R0 has been estimated to be as large as 20. And estimating R0 is directly relevant to the policy making of a random massive vaccination. So suppose that you introduce the mass vaccination, with a coverage p. Namely, 100 times p percent of the population is randomly vaccinated. Then, the effective reproduction number under the vaccination program, Rv is simply calculated by (1- p)R0 Because the 1-p fraction, 1-p of the population remains susceptible. And among all the contacts, the fraction 1-p remains effective. And if that 1-p times R0 is smaller than 1, the reproduction number is smaller than one. And therefore, we can say for sure that the major epidemic can be prevented. So dividing the both sides of this inequality, we obtain this equation. 1-p is smaller than 1/R0. And solving this equal- inequality with respect to p, we obtain this well-known relationship: P should be greater than 1-1/R0. If the vaccination coverage, P is greater than this quantity, we can say for sure that the major epidemic can be prevented. For instance the R0 of measles I told you, that the estimate was as large as 20. In other words, that indicates that, P the vaccination coverage to eradicate or eliminate measles transmission, should be maintained above 95%. And then measurement of the risk of the deaths is done- achieved by the case fatality risk, or CF-- abbreviated as CFR. We will discuss the details of the case fatality risk in a later session. But I show you the schematic illustration of the age-dependent risk of death, given infection among the confirmed cases of H1N1 2009 in United States and Mexico. So across the age, case fatality risk and its uncertain divides, or the confidence intervals are plotted and as you observe the risk of death among the children and adolescents is not very high. However all the others especially elderly have some underlying comorbidity and they are at particularly high risk of death, given influenza infection. So in summary, the part 2 set point, we studied the two points. So disaster size of a pandemic depends on transmissibility, clinical seriousness or severity, and immunity of the- at the population level. And the transmissibility is measured by two metrics: Secondary infection risk in a confined space, or the basic reproduction number in the open space. Thank you.