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probability of control for a top-20% individual was four times greater than that for a bottom-80% individual, e.g. Pr(control, top-20%)=1/4 and Pr(control, bottom- 80%)=1/16, yielding Pr(control, overall)=1/10. Under four-fold targeting, equal effort (in terms of absolute numbers controlled) is expended on top-20% and bottom-80% individuals. Targeted control was simulated as follows. For each combination of R0 and k, the cutoff value of ν dividing top-20% from bottom-80% infectiousness was established from the cdf of ν. During the simulation, after a value of ν was drawn from the gamma(R0,k) distribution for each infected individual, they were assigned to the top- 20% or bottom-80% categories. For individuals in either category, a uniform random variate on (Curtis et al. 1999) was drawn, and if it was less than the probability of control for that category then that individual’s value of ν was reset to zero. The realized number of secondary infections Zc was then generated by drawing a Poisson random variate with mean ν. For the simulations shown in Figure 4D, control was initiated in the second generation (i.e. the index case was not subject to control), representing a delay in recognition of the outbreak. Containment of an outbreak was defined as preventing it from growing to the point of a generation with 100 cases. Since a branching process that escapes control will grow without bound, results were not sensitive to this arbitrary threshold. The relative effect of targeted control (the quantity plotted in the bottom of Figure 4D) was computed as follows. The uncontrolled probability of a major outbreak for the given R0 and k was computed as 1−Pr(containment|0% control). The contribution of control efforts to containment was then calculated as: 147
Contrib(control policy) = Pr(containment|control policy)−Pr(containment|0% control). The relative effect of targeted control, plotted in Figure 4D, was then: Relative effect = Contrib(targeted control)/Contrib(random control). This quantity equaled 1 for kØ¶, since targeting has no effect on a homogeneous population, but is greater than 1 for all finite values of k. 148
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Disease transmission in heterogeneo
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Abstract Disease transmission in he
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variation is quantified from outbre
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ACKNOWLEDGEMENTS I’ve been fortun
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Engineering Research Council of Can
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Epidemic modelers face an essential
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casual contact (DCC), common usage
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HCW, and patient pools) and dynamic
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Hoffmann 2004; Shen et al. 2004)—
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Chapter Two Frequency-dependent inc
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principles and reach robust conclus
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partnership, disease and demographi
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transmission to incorporate this ph
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This result can be understood intui
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Garnett 2002). We simulated epidemi
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4. Infection-induced changes in pai
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susceptible population (Diekmann an
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epidemics, since they bring R0 clos
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equired.) For slower-moving, chroni
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addressed here. Our treatment consi
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Table 1. Results for epidemics with
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(Equations 3 and A4); the lighter l
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Figure 2 A) B) Total proportion inf
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Appendix Derivation of SI pair dens
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As described in Section 2, our goal
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can be calculated as the product of
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Chapter Three Curtailing transmissi
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egions with on-going epidemics has
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transmission to the general communi
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and Ih), as well as from off-duty t
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ours—see Diekmann & Heesterbeek (
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In all cases (Figure 2C, 2D), we id
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Sensitivity to quarantining rate (s
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precautions in the hospital (η→1
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Preventing generalized community tr
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everywhere. These robust conclusion
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increasingly significant contributi
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hospitals or wards. Future work on
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Table 1. Summary of transmission an
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formulation of our model allows for
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Figure 3 (A) Increase in cumulative
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Figure 1 74
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Figure 3 (A) (B) (C) Cumulative num
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Appendix Sensitivity to population
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symptomatic HCWs is low. Indeed, th
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E I I I I m, i m, 1 m, 2 m , 3 m ,
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for a stochastic epidemic: the prob
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progress through the age sub-compar
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We therefore wish to characterize t
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Figure S4 Distribution of (A) incub
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Figure S2 (A) (C) 92 (B) (D)
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Figure S4 (A) (B) Proportion of cas
Page 105 and 106: 1. Introduction During the global e
Page 107 and 108: quantity p0, the proportion of case
Page 109 and 110: model selection techniques, lending
Page 111 and 112: elatively mild variation in ν, sho
Page 113 and 114: cannot make inferences based on the
Page 115 and 116: low k extinction happens within the
Page 117 and 118: measured by the variance-to-mean ra
Page 119 and 120: succeed (Figure 3B). For a given re
Page 121 and 122: to lower infectiousness. Other rele
Page 123 and 124: observations for the diseases exami
Page 125 and 126: Pneumonic plague 6 outbreaks N=74 A
Page 127 and 128: s surveillance data. 90% CI: Bootst
Page 129 and 130: Figure captions Figure 1 Evidence f
Page 131 and 132: Figure 4 Impact of control measures
Page 133 and 134: Figure 2 Other Measles Influenza Ru
Page 135 and 136: Figure 4 C Reproductive number, R 1
Page 137 and 138: epresenting transmission yields a g
Page 139 and 140: We rescaled the AICc by subtracting
Page 141 and 142: for which the median of bootstrap e
Page 143 and 144: transmission due to the most infect
Page 145 and 146: an integer Z (99) such that FPoisso
Page 147 and 148: fv(u). Demographic stochasticity in
Page 149 and 150: original process is eliminated with
Page 151 and 152: For RA control, with a random propo
Page 153 and 154: ∞ ( , ) ∫ ( ) ( ). k−1 −t w
Page 155: * and for ν < ν : ∞ ∫ ν C 1
Page 159 and 160: ange of the first disease generatio
Page 161 and 162: Figure S1 MLE estimates of k 10 1 0
Page 163 and 164: Figure S2 (cont) D E F k=inf k=4 k=
Page 165 and 166: Figure S4 Relative frequency 0.4 0.
Page 167 and 168: SARS (March 12) and the imposition
Page 169 and 170: Transmission was predominantly by i
Page 171 and 172: Monkeypox, Zaire 1980-1984 (Jezek e
Page 173 and 174: Rubella, Hawaii 1970 (Hattis et al.
Page 175 and 176: tracing, with the stated goal of de
Page 177 and 178: Table S1. Superspreading events in
Page 179 and 180: Measles 250 Dance party ?M First ar
Page 181 and 182: SARS 187+ Apartment block 26M Amoy
Page 183 and 184: SARS 33 Hospital 62W Undiagnosed: S
Page 185 and 186: SARS 24/2* Home, emergency room, IC
Page 187 and 188: Streptococcus group A (type 1) 100+
Page 189 and 190: References Abbot, P. and L. M. Dill
Page 191 and 192: Caswell, H. (2001) Matrix Populatio
Page 193 and 194: Evatt, B. L., W. R. Dowdle, M. John
Page 195 and 196: Health Canada (2003) Summary of Sev
Page 197 and 198: Kretzschmar, M. (2000). Sexual netw
Page 199 and 200: McCallum, H., N. Barlow and J. Hone
Page 201 and 202: Taylor, H. M. and S. Karlin (1998).
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