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1 GIS Theory and Applcatons of GIS, 006, Vol. 14, No., pp A Rsk Populaton Estmaton Method for Interregonal Comparson: An analyss on the nfluenza data reported by the sur vellance system Yusuke KATAOKA, Yasush ASAMI, Yuk TADA and Ken OSAKA Abstract: Ths study proposes a rsk populaton estmaton method based on the reported counts of patents of nfluenza n each area. A rsk populaton estmaton method s developed by regardng the sum of the weekly observatons n a season followng the bnomal dstrbuton, whch enables estmatons of populaton parameters and probablty parameters through maxmum lkelhood method. The results show that the estmated nfecton probablty s consstently correlated wth populaton densty, and the estmated populaton parameters n areas wth hgh populatons are obvously small n comparson wth the proporton of populaton. Keywords: Epdemologcal Survellance of Infectous Dseases maxmum lkelhood methodnfluenzapopulaton densty bnomal dstrbuton Center for Spatal Informaton Scence, Unversty of Tokyo E-malukataoka@css.u-tokyo.ac.p ,000 Clff et al.1986 Vboud et al.003

2 GIS GIS Myung003 1,,, M 1,,, T x, x, n p Dawson-Saunders and Trapp, 1994 M x, 1 n p n p1p fx

3 f `x ; n p, n p _ 1-p, 1 _ x, -np $ exp* - 4 r$ n p _ 1-p n p _ 1-p 1 L % M % T L ln f `x, ; n p, n p _ 1-p M T ln% % 1 r$ np ^1- p1h _ x, -np # exp* - 4 n p _ 1-p M T ln 1 r$ n p _ 1-p M T _ x, -np - n p _ 1-p 1 n p 1,,,T ln n L 0 3 ln L 0 ^ 1,, g, Th p 4 3 ln n L T T x, np n n p _ 1-p 5 n x T, T p - T+ T + 4$ $ p _ 1 p - 1-p n p ^n> 0h 6 T $ 1-p 4 ln L _ 1-p M - p p _ 1-p M _ n p- x, `np+ _ 1-px, - 0 n p _ 1-p 7 ^1-p M n p x n p 1 p x h M _ -, ` + _ -, p 1 p _ - n p _ 1-p _ 1-p M p _ 1-p 1 _ - p x T, T p - T+ T + 4$ $ p 1 p 1 p M _ - - # T p $ 1-p 1 M 1-p + x, + _ 1 - p p _ 1-p T p $ M 1-p # T x, T p - T+ T + 4$ $ p _ 1 p - 1-p $ x, p 1,,,T p p 1, p,, p T 6 n

4 Chu et al.001 Two-Step BICBayesan Informaton Crtera 4 Two-Step BIC BIC 5 3 7

5 BIC ズ ン の 罹 患 率 p1, p, p3 の 数 値 解 を Trust-Regon Dogleg 法 Conn et al., 000 を用いて求めた そ の結果 各ブロックについて得られた罹患率と 全 医療圏を分類せずに一括して推定した場合のものと を併せて以下の図 6 に示す クラスターの番号は図 5 にならう 図 6 より 各ブロックの罹患率の 3 シーズン間の ᢙ 推移から 罹患率の推移が図 1 の報告数の推移と対 応していることと 人口密度が高いブロックほど罹 図 4 クラスター数と BIC 患率の推定値が高い傾向があることが言える また 都区部の都心近郊 および大阪府の堺市以南の泉州 ブロック間で見られる罹患率の差異については 人 地域が最も人口密度が高いグループとなり それら 口密度の高い地域の感染率が高いという 現状の理 の周辺地域が 番目に そしてその他の地域が 3 番 解を裏付ける結果として解釈できよう それぞれの 目のグループとなるといった地理的分布を呈してい 罹患率の推定値をみると 現実のそれとは幾分高い る また 人口の多い大阪市や名古屋市が 人口密 印象を与えているが 前述のとおりここでいう罹患 度が低いクラスターに分類されている理由は 二次 率とは 潜在的に感染するおそれのある人たちが感 医療圏の各圏域面積が影響していることによる 染する確率であるため 通常の罹患率とは若干性質 が異なるものである ᖚ 1 䉪䊤䉴䉺䊷㪈 䉪䊤䉴䉺䊷㪉 䉪䊤䉴䉺䊷㪊 km ) 00 (䭴䮴䮨䯃䮏䮲 㧔 ญኒᐲૐ䊶335 㧕㩷 図 6 ブロック別の罹患率 推定値 の推移 㧔 ญኒᐲਛ䊶㩷 18 㧕 3㧔 ญኒᐲ㜞䊶㩷 㩷 7 㧕 さらに 得られた p1, p, p3 を式 6 に代入すれば 図 5 地域の分類 全ての二次医療圏の報告数のリスク人口が得られ る 図 7 は得られた推定値の頻度分布を表し また図 3 3 リスク人口および罹患率の推定 前項までの考察をふまえ 分類されたそれぞれの 地域ブロックで推定モデルを適用し リスク人口と 8 は推定値の地理的分布について図示したものとな る 両図において 階級幅は原則的に,500 人とした 罹患率の推定値を求める 報告数が 3 シーズン分あることから 節の式 8 は 3 元非線形連立方程式となる そこで まず各シー まず 図 7 では定点の圏域人口は広範囲にわたって 分布すること そして図 8 からは 概して中山間地 98Ȫ6ȫ

6 , ~,500,501 ~ 5,000 5,001 ~ 7,500 7,501 ~ 10,000 10,001 ~, ( km )

7

8 1 HP html , BIC 6 BIC 003 GIS GIS Chu, T., Fang, J., Wang, Y., and Jers, C.001A Robust and Scalable Clusterng Algorthm for Mxed Type Attrbutes n Large Database Envronment. Proceedngs of the Seventh ACM SIGKDD Internatonal Conference on Knowledge Dscovery and Data Mnng, Clff, A.D., Haggett, P. and Ord, J.K.1986Spatal Aspects of nfluenza epdemcs. London: Pon Conn, A.R., Gould, N.I.M. and Tont, P.L.000Trust- Regon Methods. Phladelpha: SIAM. Dawson-Saunders, B. and Trapp, R.G.1994Basc and Clncal Bostatstcs second edton. Norwalk: Appleton & Lange. Myung, I.J. 003 Tutoral on maxmum lkelhood estmaton. Journal of Mathematcal Psychology, 47, Vboud, C., Boëlle, P.Y., Carrat, F., Valleron, A.J. and Flahault, A. 003 Predcton of the Spread of Influenza Epdemcs by the Method of Analogues. Amercan Journal of Epdemology, 158,

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